SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation

The real-time requirement for image segmentation in laparoscopic surgical assistance systems is extremely high. Although traditional deep learning models can ensure high segmentation accuracy, they suffer from a large computational burden. In the practical setting of most hospitals, where powerful computing resources are lacking, these models cannot meet the real-time computational demands. We propose a novel network SwinD-Net based on Skip connections, incorporating Depthwise separable convolutions and Swin Transformer Blocks. To reduce computational overhead, we eliminate the skip connection in the first layer and reduce the number of channels in shallow feature maps. Additionally, we introduce Swin Transformer Blocks, which have a larger computational and parameter footprint, to extract global information and capture high-level semantic features. Through these modifications, our network achieves desirable performance while maintaining a lightweight design. We conduct experiments on the CholecSeg8k dataset to validate the effectiveness of our approach. Compared to other models, our approach achieves high accuracy while significantly reducing computational and parameter overhead. Specifically, our model requires only 98.82 M floating-point operations (FLOPs) and 0.52 M parameters, with an inference time of 47.49 ms per image on a CPU. Compared to the recently proposed lightweight segmentation network UNeXt, our model not only outperforms it in terms of the Dice metric but also has only 1/3 of the parameters and 1/22 of the FLOPs. In addition, our model achieves a 2.4 times faster inference speed than UNeXt, demonstrating comprehensive improvements in both accuracy and speed. Our model effectively reduces parameter count and computational complexity, improving the inference speed while maintaining comparable accuracy. The source code will be available at https://github.com/ouyangshuiming/SwinDNet.

Parameter-efficient framework for surgical action triplet recognition

PURPOSE

Surgical action triplet recognition is a clinically significant yet challenging task. It provides surgeons with detailed information about surgical scenarios, thereby facilitating clinical decision-making. However, the high similarity among action triplets presents a formidable obstacle to recognition. To enhance accuracy, prior methods necessitated the utilization of larger models, thereby incurring a considerable computational burden.

METHODS

We propose a novel framework known as the Lite and Mega Models (LAM). It comprises a CNN-based fully fine-tuned model (LAM-Lite) and a parameter-efficient fine-tuned model based on the foundation model using Transformer architecture (LAM-Mega). Temporal multi-label data augmentation is introduced for extracting robust class-level features.

RESULTS

Our study demonstrates that LAM outperforms prior methods across various parameter scales on the CholecT50 dataset. Using fewer tunable parameters, LAM achieves a mean average precision (mAP) of 42.1%, a 3.6% improvement over the previous state of the art.

CONCLUSION

Leveraging effective structural design and robust capabilities of the foundational model, our proposed approach successfully strikes a balance between accuracy and computational efficiency. The source code is accessible at https://github.com/Lycus99/LAM .

EndoSRR: a comprehensive multi-stage approach for endoscopic specular reflection removal

PURPOSE

Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal.

METHODS

We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images. EndoSRR comprises two stages: reflection detection and reflection region inpainting. In the reflection detection stage, we adapt and fine-tune the segment anything model (SAM) using a weakly labeled dataset, achieving an accurate reflection mask. For reflective region inpainting, we employ LaMa, a fast Fourier convolution-based model trained on a 4.5M-image dataset, enabling effective inpainting of arbitrarily shaped reflection regions. Lastly, we introduce an iterative optimization strategy for dual pre-trained models to refine the results of specular reflection removal, named DPMIO.

RESULTS

Utilizing the SCARED-2019 dataset, our approach surpasses state-of-the-art methods in both qualitative and quantitative evaluations. Qualitatively, our method excels in accurately detecting reflective regions, yielding more natural and realistic inpainting results. Quantitatively, our method demonstrates superior performance in both segmentation evaluation metrics (IoU, E-measure, etc.) and image inpainting evaluation metrics (PSNR, SSIM, etc.).

CONCLUSION

The experimental results underscore the significance of proficient endoscopic specular reflection removal for enhancing visual perception and downstream tasks. The methodology and results presented in this study are poised to catalyze advancements in specular reflection removal, thereby augmenting the accuracy and safety of minimally invasive surgery.

MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma

BACKGROUND

Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult.

PURPOSE

To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery.

MATERIAL AND METHODS

A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated.

RESULTS

The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively.

CONCLUSION

The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.

An MRI-only Three-dimensional Cephalometry Protocol based on the Integrated and Modular Architecture of the Human Head

BACKGROUND

Currently, three-dimensional cephalometry measurements are mainly based on cone beam computed tomography (CBCT), which has limitations of ionizing radiation, lack of soft tissue information, and lack of standardization of median sagittal plane establishment.

OBJECTIVES

This study investigated magnetic resonance imaging (MRI)-only based 3D cephalometry measurement based on the integrated and modular characteristics of the human head.

METHODS

Double U-Net CycleGAN was used for CT image synthesis from MRI. This method enabled the synthesis of a CT-like image from MRI and measurements were made using 3D slicer registration and fusion.

RESULTS

A protocol for generating and optimizing MRI-based synthetic CT was described and found to meet the precision requirements of 3D head measurement using MRI midline positioning methods reported in neuroscience to establish the median sagittal plane. An MRI-only reference frame and coordinate system were established enabling an MRI-only cephalometric analysis protocol that combined the dual advantages of soft and hard tissue display. The protocol was devised using data from a single volunteer and validation data from a larger sample remains to be collected.

CONCLUSION

The reported method provided a new protocol for MRI-only cephalometric analysis of craniofacial growth and development, malformation occurrence, treatment planning, and outcomes.

A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric

BACKGROUND

The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training.

PURPOSE

The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-based methods.

METHODS

We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imaging results.

RESULTS

The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel, respectively.

CONCLUSIONS

The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurate registration.

MT-FiST: A Multi-Task Fine-Grained Spatial-Temporal Framework for Surgical Action Triplet Recognition

Surgical action triplet recognition plays a significant role in helping surgeons facilitate scene analysis and decision-making in computer-assisted surgeries. Compared to traditional context-aware tasks such as phase recognition, surgical action triplets, comprising the instrument, verb, and target, can offer more comprehensive and detailed information. However, current triplet recognition methods fall short in distinguishing the fine-grained subclasses and disregard temporal correlation in action triplets. In this article, we propose a multi-task fine-grained spatial-temporal framework for surgical action triplet recognition named MT-FiST. The proposed method utilizes a multi-label mutual channel loss, which consists of diversity and discriminative components. This loss function decouples global task features into class-aligned features, enabling the learning of more local details from the surgical scene. The proposed framework utilizes partial shared-parameters LSTM units to capture temporal correlations between adjacent frames. We conducted experiments on the CholecT50 dataset proposed in the MICCAI 2021 Surgical Action Triplet Recognition Challenge. Our framework is evaluated on the private test set of the challenge to ensure fair comparisons. Our model apparently outperformed state-of-the-art models in instrument, verb, target, and action triplet recognition tasks, with mAPs of 82.1% (+4.6%), 51.5% (+4.0%), 45.50% (+7.8%), and 35.8% (+3.1%), respectively. The proposed MT-FiST boosts the recognition of surgical action triplets in a context-aware surgical assistant system, further solving multi-task recognition by effective temporal aggregation and fine-grained features.

SDA-CLIP: surgical visual domain adaptation using video and text labels

Background

Surgical action recognition is an essential technology in context-aware-based autonomous surgery, whereas the accuracy is limited by clinical dataset scale. Leveraging surgical videos from virtual reality (VR) simulations to research algorithms for the clinical domain application, also known as domain adaptation, can effectively reduce the cost of data acquisition and annotation, and protect patient privacy.

Methods

We introduced a surgical domain adaptation method based on the contrastive language-image pretraining model (SDA-CLIP) to recognize cross-domain surgical action. Specifically, we utilized the Vision Transformer (ViT) and Transformer to extract video and text embeddings, respectively. Text embedding was developed as a bridge between VR and clinical domains. Inter- and intra-modality loss functions were employed to enhance the consistency of embeddings of the same class. Further, we evaluated our method on the MICCAI 2020 EndoVis Challenge SurgVisDom dataset.

Results

Our SDA-CLIP achieved a weighted F1-score of 65.9% (+18.9%) on the hard domain adaptation task (trained only with VR data) and 84.4% (+4.4%) on the soft domain adaptation task (trained with VR and clinical-like data), which outperformed the first place team of the challenge by a significant margin.

Conclusions

The proposed SDA-CLIP model can effectively extract video scene information and textual semantic information, which greatly improves the performance of cross-domain surgical action recognition. The code is available at https://github.com/Lycus99/SDA-CLIP.

Augmented reality-assisted navigation system contributes to better intraoperative and short-time outcomes of laparoscopic pancreaticoduodenectomy: a retrospective cohort study

BACKGROUND

Augmented reality (AR)-assisted navigation system are currently good techniques for hepatectomy; however, its application and efficacy for laparoscopic pancreatoduodenectomy have not been reported. This study sought to focus on and evaluate the advantages of laparoscopic pancreatoduodenectomy guided by the AR-assisted navigation system in intraoperative and short-time outcomes.

METHODS

Eighty-two patients who underwent laparoscopic pancreatoduodenectomy from January 2018 to May 2022 were enrolled and divided into the AR and non-AR groups. Clinical baseline features, operation time, intraoperative blood loss, blood transfusion rate, perioperative complications, and mortality were analyzed.

RESULTS

AR-guided laparoscopic pancreaticoduodenectomy was performed in the AR group ( n =41), whereas laparoscopic pancreatoduodenectomy was carried out routinely in the non-AR group ( n =41). There was no significant difference in baseline data between the two groups ( P >0.05); Although the operation time of the AR group was longer than that of the non-AR group (420.15±94.38 vs. 348.98±76.15, P <0.001), the AR group had a less intraoperative blood loss (219.51±167.03 vs. 312.20±195.51, P =0.023), lower blood transfusion rate (24.4 vs. 65.9%, P <0.001), lower occurrence rates of postoperative pancreatic fistula (12.2 vs. 46.3%, P =0.002) and bile leakage (0 vs. 14.6%, P =0.026), and shorter postoperative hospital stay (11.29±2.78 vs. 20.04±11.22, P <0.001) compared with the non-AR group.

CONCLUSION

AR-guided laparoscopic pancreatoduodenectomy has significant advantages in identifying important vascular structures, minimizing intraoperative damage, and reducing postoperative complications, suggesting that it is a safe, feasible method with a bright future in the clinical setting.

Efficacy and Interpretability Analysis of Noninvasive Imaging Based on Computed Tomography in Patients with Hepatocellular Carcinoma After Initial Transarterial Chemoembolization

RATIONALE AND OBJECTIVES

The objective of this study is to accurately and timely assess the efficacy of patients with hepatocellular carcinoma (HCC) after the initial transarterial chemoembolization (TACE).

MATERIALS AND METHODS

This retrospective study consisted of 279 patients with HCC in Center 1, who were split into training and validation cohorts in the ratio of 4:1, and 72 patients in Center 2 as an external testing cohort. Radiomics signatures both in the arterial phase and venous phase of contrast-enhanced computed tomography images were selected by univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression to build the predicting models. The clinical model and combined model were constructed by independent risk factors after univariate and multivariate logistic regression analysis. The biological interpretability of radiomics signatures correlating transcriptome sequencing data was explored using publicly available data sets.

RESULTS

A total of 31 radiomics signatures in the arterial phase and 13 radiomics signatures in the venous phase were selected to construct Radscore_arterial and Radscore_venous, respectively, which were independent risk factors. After constructing the combined model, the area under the receiver operating characteristic curve in three cohorts was 0.865, 0.800, and 0.745, respectively. Through correlation analysis, 11 radiomics signatures in the arterial phase and 4 radiomics signatures in the venous phase were associated with 8 and 5 gene modules, respectively (All P < .05), which enriched some pathways closely related to tumor development and proliferation.

CONCLUSION

Noninvasive imaging has considerable value in predicting the efficacy of patients with HCC after initial TACE. The biological interpretability of the radiological signatures can be mapped at the micro level.

Anatomical network modules of the human central nervous-craniofacial skeleton system

Anatomical network analysis (AnNA) is a systems biological framework based on network theory that enables anatomical structural analysis by incorporating modularity to model structural complexity. The human brain and facial structures exhibit close structural and functional relationships, suggestive of a co-evolved anatomical network. The present study aimed to analyze the human head as a modular entity that comprises the central nervous system, including the brain, spinal cord, and craniofacial skeleton. An AnNA model was built using 39 anatomical nodes from the brain, spinal cord, and craniofacial skeleton. The linkages were identified using peripheral nerve supply and direct contact between structures. The Spinglass algorithm in the igraph software was applied to construct a network and identify the modules of the central nervous system-craniofacial skeleton anatomical network. Two modules were identified. These comprised an anterior module, which included the forebrain, anterior cranial base, and upper-middle face, and a posterior module, which included the midbrain, hindbrain, mandible, and posterior cranium. These findings may reflect the genetic and signaling networks that drive the mosaic central nervous system and craniofacial development and offer important systems biology perspectives for developmental disorders of craniofacial structures.

Associations Between Primary Residence and Mental Health in Global Marginalized Populations

Scholars suggest that marginalized people in non-urban areas experience higher distress levels and fewer psychosocial resources than in urban areas. Researchers have yet to test whether precise proximity to urban centers is associated with mental health for marginalized populations. We recruited 1733 people who reported living in 45 different countries. Participants entered their home locations and completed measures of anxiety, depression, social support, and resilience. Regression and thematic analyses were used to determine what role distance from legislative and urban centers may play in mental health when marginalized people were disaggregated. Greater distance from legislative center predicted higher anxiety and resilience. Greater distance from urban center also predicted more resilience. Thematic analyses yielded five categories (e.g., safety, connection) that further illustrated the impact of geographic location on health. Implications for community mental health are discussed including the need to better understand and further expand resilience in rural areas.

Measurement and assessment of grief in a large international sample

BACKGROUND

In 2022, the International Classification of Diseases (ICD-11) and an update of the Diagnostic Statistical Manual of Mental Disorders (DSM 5 TR) were released for implementation worldwide and now include the new Prolonged Grief Disorder (PGD). The newest definition of PGD is based on robust clinical research from the Global North yet until now has not been tested for global applicability.

METHODS

The current study assesses the new PGD ICD-11 criteria in a large international sample of 1393 bereaved adults. The majority of the sample was included from the USΑ. Additionally, we conduct a sub-sample analysis to evaluate the psychometric properties, probable caseness of PGD, and differences in network structure across three regions of residency (USA, Greece-Cyprus, Turkey-Iran).

RESULTS

The psychometric validity and reliability of the 33-item International Prolonged Grief Disorder Scale (IPGDS) were confirmed across the whole sample and for each regional group. Using the strict diagnostic algorithm, the probable caseness for PGD for the whole sample was 3.6 %. Probable caseness was highest for the Greece-Cyprus group (6.9 %) followed by Turkey-Iran (3.2 %) and the USA (2.8 %). Finally, the network structure of the IPGDS standard items and cultural supplement items (total of 33 items) confirmed the strong connection between central items of PGD, and revealed unique network connections within the regional groups.

LIMITATIONS

Future research is encouraged to include larger sample sizes and a more systematic assessment of culture.

CONCLUSION

Overall, our findings confirm the global applicability of the new ICD-11 PGD disorder definition as evaluated through the newly developed IPGDS. This scale includes culturally sensitive grief symptoms that may improve clinical precision and decision-making.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network

Background

Methods based on the combination of transformer and convolutional neural networks (CNNs) have achieved impressive results in the field of medical image segmentation. However, most of the recently proposed combination segmentation approaches simply treat transformers as auxiliary modules which help to extract long-range information and encode global context into convolutional representations, and there is a lack of investigation on how to optimally combine self-attention with convolution.

Methods

We designed a novel transformer block (MRFormer) that combines a multi-head self-attention layer and a residual depthwise convolutional block as the basic unit to deeply integrate both long-range and local spatial information. The MRFormer block was embedded between the encoder and decoder in U-Net at the last two layers. This framework (UMRFormer-Net) was applied to the segmentation of three-dimensional (3D) pancreas, and its ability to effectively capture the characteristic contextual information of the pancreas and surrounding tissues was investigated.

Results

Experimental results show that the proposed UMRFormer-Net achieved accuracy in pancreas segmentation that was comparable or superior to that of existing state-of-the-art 3D methods in both the Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) dataset and the public Medical Segmentation Decathlon dataset (self-division). UMRFormer-Net statistically significantly outperformed existing transformer-related methods and state-of-the-art 3D methods (P<0.05, P<0.01, or P<0.001), with a higher Dice coefficient (85.54% and 77.36%, respectively) or a lower 95% Hausdorff distance (4.05 and 8.34 mm, respectively).

Conclusions

UMRFormer-Net can obtain more matched and accurate segmentation boundary and region information in pancreas segmentation, thus improving the accuracy of pancreas segmentation. The code is available at https://github.com/supersunshinefk/UMRFormer-Net.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

PURPOSE

Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill.

METHODS

To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment.

RESULTS

F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team).

CONCLUSION

Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

Intraoperative laparoscopic liver surface registration with preoperative CT using mixing features and overlapping region masks

PURPOSE

Laparoscopic liver resection is a minimal invasive surgery. Augmented reality can map preoperative anatomy information extracted from computed tomography to the intraoperative liver surface reconstructed from stereo 3D laparoscopy. However, liver surface registration is particularly challenging as the intraoperative surface is only partially visible and suffers from large liver deformations due to pneumoperitoneum. This study proposes a deep learning-based robust point cloud registration network.

METHODS

This study proposed a low overlap liver surface registration algorithm combining local mixed features and global features of point clouds. A learned overlap mask is used to filter the non-overlapping region of the point cloud, and a network is used to predict the overlapping region threshold to regulate the training process.

RESULTS

We validated the algorithm on the DePoLL (the Deformable Porcine Laparoscopic Liver) dataset. Compared with the baseline method and other state-of-the-art registration methods, our method achieves minimum target registration error (TRE) of 19.9 ± 2.7 mm.

CONCLUSION

The proposed point cloud registration method uses the learned overlapping mask to filter the non-overlapping areas in the point cloud, then the extracted overlapping area point cloud is registered according to the mixed features and global features, and this method is robust and efficient in low-overlap liver surface registration.

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

[Prolonged epidural labor analgesia increases risks of epidural analgesia failure for conversion to cesarean section]

OBJECTIVE

To explore the effect of epidural labor analgesia duration on the outcomes of different anesthetic approaches for conversion to cesarean section.

METHODS

We retrospectively collected the clinical data of pregnant women undergoing conversion from epidural labor analgesia to cesarean section at Sichuan Maternal and Child Health Hospital and Jinjiang District Maternal and Child Health Care Hospital between July, 2019 and June, 2020. For cesarean section, the women received epidural anesthesia when the epidural catheter was maintained in correct position with effective analgesia, spinal anesthesia at the discretion of the anesthesiologists, or general anesthesia in cases requiring immediate cesarean section or following failure of epidural anesthesia or spinal anesthesia. Receiver-operating characteristic curve analysis was performed to determine the cutoff value of the analgesia duration using Youden index. The women were divided into two groups according to the cut off value for analyzing the relative risk using cross tabulations.

RESULTS

A total of 820 pregnant women undergoing conversion to cesarean section were enrolled in this analysis, including 615 (75.0%) in epidural anesthesia group, 186 (22.7%) in spinal anesthesia group, and 19 (2.3%) in general anesthesia group; none of the women experienced failure of epidural or spinal anesthesia. The mean anesthesia duration was 8.2±4.7 h in epidural anesthesia, 10.6±5.1 h in spinal anesthesia group, and 6.7 ± 5.2 h in general anesthesia group. Multivariate logistic regression analysis showed that prolongation of analgesia duration by 1 h (OR=1.094, 95% CI: 1.057-1.132, P < 0.001) and an increase of cervical orifice by 1 cm (OR=1.066, 95% CI: 1.011-1.124, P=0.017) were independent risk factors for epidural analgesia failure. The cutoff value of analgesia duration was 9.5 h, and beyond that duration the relative risk of receiving spinal anesthesia was 1.204 (95% CI: 1.103-2.341, P < 0.001).

CONCLUSION

Prolonged epidural labor analgesia increases the risk of failure of epidural analgesia for conversion to epidural anesthesia. In cases with an analgesia duration over 9.5 h, spinal anesthesia is recommended if immediate cesarean section is not required.

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

Unsupervised learning of depth estimation from imperfect rectified stereo laparoscopic images

BACKGROUND

Learning-based methods have achieved remarkable performances on depth estimation. However, the premise of most self-learning and unsupervised learning methods is built on rigorous, geometrically-aligned stereo rectification. The performances of these methods degrade when the rectification is not accurate. Therefore, we explore an approach for unsupervised depth estimation from stereo images that can handle imperfect camera parameters.

METHODS

We propose an unsupervised deep convolutional network that takes rectified stereo image pairs as input and outputs corresponding dense disparity maps. First, a new vertical correction module is designed for predicting a correction map to compensate for the imperfect geometry alignment. Second, the left and right images, which are reconstructed based on the input image pair and corresponding disparities as well as the vertical correction maps, are regarded as the outputs of the generative term of the generative adversarial network (GAN). Then, the discriminator term of the GAN is used to distinguish the reconstructed images from the original inputs to force the generator to output increasingly realistic images. In addition, a residual mask is introduced to exclude pixels that conflict with the appearance of the original image in the loss calculation.

RESULTS

The proposed model is validated on the publicly available Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) dataset and the average MAE is 3.054 mm.

CONCLUSION

Our model can effectively handle imperfect rectified stereo images for depth estimation.

A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets

BACKGROUND

Most existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions, different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study.

METHODS

The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3) One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder.

RESULTS

Model trained on different datasets has different segmentation performance. The prediction accuracy between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets.

CONCLUSIONS

(1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

Purpose

To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer.

Methods

A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram.

Results

The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987).

Conclusion

A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03137-1.

Morphologic Change of In Vivo Porcine Liver Under 13 mm Hg Pneumoperitoneum Pressure

BACKGROUND

Clinically, the total and residual liver volume must be accurately calculated before major hepatectomy. However, liver volume might be influenced by pneumoperitoneum during surgery. Changes in liver volume change also affect the accuracy of simulation and augmented reality navigation systems, which are commonly first validated in animal models. In this study, the morphologic changes in porcine livers in vivo under 13 mm Hg pneumoperitoneum pressure were investigated.

MATERIALS AND METHODS

Twenty male pigs were scanned with contrast-enhanced computed tomography without pneumoperitoneum and with 13 mm Hg pneumoperitoneum pressure.

RESULTS

The surface area and volume of the liver and the vascular diameter of the aortic lumen, inferior vena cava lumen, and portal vein lumen were measured. There were statistically significant differences in the surface area and volume of the liver (P=0.000), transverse diameter of the portal vein (P=0.038), longitudinal diameter of the inferior vena cava (P=0.033), longitudinal diameter of the portal vein (P=0.036), vascular cross-sectional area of the inferior vena cava (P=0.028), and portal vein (P=0.038) before and after 13 mm Hg pneumoperitoneum pressure.

CONCLUSIONS

This study indicated that the creation of pneumoperitoneum at 13 mm Hg pressure in a porcine causes liver morphologic alterations affecting the area and volume, as well as the diameter of a blood vessel.

Against spatial-temporal discrepancy: contrastive learning-based network for surgical workflow recognition

PURPOSE

Automatic workflow recognition from surgical videos is fundamental and significant for developing context-aware systems in modern operating rooms. Although many approaches have been proposed to tackle challenges in this complex task, there are still many problems such as the fine-grained characteristics and spatial-temporal discrepancies in surgical videos.

METHODS

We propose a contrastive learning-based convolutional recurrent network with multi-level prediction to tackle these problems. Specifically, split-attention blocks are employed to extract spatial features. Through a mapping function in the step-phase branch, the current workflow can be predicted on two mutual-boosting levels. Furthermore, a contrastive branch is introduced to learn the spatial-temporal features that eliminate irrelevant changes in the environment.

RESULTS

We evaluate our method on the Cataract-101 dataset. The results show that our method achieves an accuracy of 96.37% with only surgical step labels, which outperforms other state-of-the-art approaches.

CONCLUSION

The proposed convolutional recurrent network based on step-phase prediction and contrastive learning can leverage fine-grained characteristics and alleviate spatial-temporal discrepancies to improve the performance of surgical workflow recognition.

Augmented Reality Navigation for Stereoscopic Laparoscopic Anatomical Hepatectomy of Primary Liver Cancer: Preliminary Experience

Background

Accurate determination of intrahepatic anatomy remains challenging for laparoscopic anatomical hepatectomy (LAH). Laparoscopic augmented reality navigation (LARN) is expected to facilitate LAH of primary liver cancer (PLC) by identifying the exact location of tumors and vessels. The study was to evaluate the safety and effectiveness of our independently developed LARN system in LAH of PLC.

Methods

From May 2018 to July 2020, the study included 85 PLC patients who underwent three-dimensional (3D) LAH. According to whether LARN was performed during the operation, the patients were divided into the intraoperative navigation (IN) group and the non-intraoperative navigation (NIN) group. We compared the preoperative data, perioperative results and postoperative complications between the two groups, and introduced our preliminary experience of this novel technology in LAH.

Results

There were 44 and 41 PLC patients in the IN group and the NIN group, respectively. No significant differences were found in preoperative characteristics and any of the resection-related complications between the two groups (All P > 0.05). Compared with the NIN group, the IN group had significantly less operative bleeding (P = 0.002), lower delta Hb% (P = 0.039), lower blood transfusion rate (P < 0.001), and reduced postoperative hospital stay (P = 0.003). For the IN group, the successful fusion of simulated surgical planning and operative scene helped to determine the extent of resection.

Conclusions

The LARN contributed to the identification of important anatomical structures during LAH of PLC. It reduced vascular injury and accelerated postoperative recovery, showing a potential application prospects in liver surgery.

MDCC-Net: Multiscale double-channel convolution U-Net framework for colorectal tumor segmentation

PURPOSE

Multiscale feature fusion is a feasible method to improve tumor segmentation accuracy. However, current multiscale networks have two common problems: 1. Some networks only allow feature fusion between encoders and decoders of the same scale. It is obvious that such feature fusion is not sufficient. 2. Some networks have too many dense skip connections and too much nesting between the coding layer and the decoding layer, which causes some features to be lost and means that not enough information will be learned from multiple scales. To overcome these two problems, we propose a multiscale double-channel convolution U-Net (MDCC-Net) framework for colorectal tumor segmentation.

METHODS

In the coding layer, we designed a dual-channel separation and convolution module and then added residual connections to perform multiscale feature fusion on the input image and the feature map after dual-channel separation and convolution. By fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

RESULTS

The segmentation results show that our proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 83.57%, which is an improvement of 9.59%, 6.42%, and 1.57% compared with nnU-Net, U-Net, and U-Net++, respectively.

CONCLUSION

The experimental results show that our proposed method has good performance in the segmentation of colorectal tumors and is close to the expert level. The proposed method has potential clinical applicability.

Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma

Objective

This study aimed to build and evaluate a radiomics feature-based model for the preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma.

Methods

A total of 145 patients were retrospectively included in the study pool, and the patients were divided randomly into two independent cohorts with a ratio of 7:3 (training cohort: n = 101, validation cohort: n = 44). For a pilot study of this predictive model another 18 patients were recruited into this study. A total of 1,231 computed tomography (CT) image features of the liver parenchyma without tumors were extracted from portal-phase CT images. A least absolute shrinkage and selection operator (LASSO) logistic regression was applied to build a radiomics score (Rad-score) model. Afterwards, a nomogram, including Rad-score as well as other clinicopathological risk factors, was established with a multivariate logistic regression model. The discrimination efficacy, calibration efficacy, and clinical utility value of the nomogram were evaluated.

Results

The Rad-score scoring model could predict MVI with the area under the curve (AUC) of 0.637 (95% CI, 0.516–0.758) in the training cohort as well as of 0.583 (95% CI, 0.395–0.770) in the validation cohort; however, the aforementioned discriminative approach could not completely outperform those existing predictors (alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin). The individual predictive nomogram which included the Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin showed a better discrimination efficacy with AUC of 0.865 (95% CI, 0.786–0.944), which was higher than the conventional methods’ AUCs (nomogram vs Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin at P < 0.001, P = 0.025, P < 0.001, and P = 0.001, respectively). When applied to the validation cohort, the nomogram discrimination efficacy was still outbalanced those above mentioned three remaining methods (AUC: 0.705; 95% CI, 0.537–0.874). The calibration curves of this proposed method showed a satisfying consistency in both cohorts. A prospective pilot analysis showed that the nomogram could predict MVI with an AUC of 0.844 (95% CI, 0.628–1.000).

Conclusions

The radiomics feature-based predictive model improved the preoperative prediction of MVI in HCC patients significantly. It could be a potentially valuable clinical utility.

ILK participates in renal interstitial fibrosis by altering the phenotype of renal tubular epithelial cells via TGF-β1/smad pathway

OBJECTIVE

To explore the specific role of ILK (integrin-linked kinase) in regulating renal fibrosis and its underlying mechanism.

MATERIALS AND METHODS

NRK-52E cells were induced by transforming growth factor-β1 (TGF-β1) for observing phenotype change. Renal tubular epithelial cell marker, fibrosis marker and expression level of ILK in NRK-52E cells were also detected. After overexpression of ILK, phenotype change of NRK-52E cells was observed. For in vivo experiments, we constructed UUO (unilateral ureteral obstruction) model in CD1 mice. Renal tubular epithelial cell marker, fibrosis marker and expression level of ILK in UUO mice were detected. The regulatory effect of ILK on renal fibrosis was detected after injection of ILK overexpression plasmid. Western blot was performed to detect related genes in the TGF-β1/smad pathway.

RESULTS

Accompanied by the TGF-β1-induced phenotype change in NRK-52E cells, both mRNA and protein levels of ILK were upregulated. Overexpression of ILK remarkably stimulated the phenotype change in NRK-52E cells. Similarly, ILK was highly expressed in UUO mice. Renal fibrosis was aggravated after injection of ILK overexpression plasmid in UUO mice. Western blot results showed that expressions of p-smad3 and smad3 were upregulated during the process of renal fibrosis.

CONCLUSIONS

ILK is upregulated during the process of renal fibrosis. ILK participates in the development of renal fibrosis by altering phenotypes of renal tubular epithelial cells via a TGF-β1/smad pathway.

Augmented reality navigation for liver resection with a stereoscopic laparoscope

OBJECTIVE

Understanding the three-dimensional (3D) spatial position and orientation of vessels and tumor(s) is vital in laparoscopic liver resection procedures. Augmented reality (AR) techniques can help surgeons see the patient's internal anatomy in conjunction with laparoscopic video images.

METHOD

In this paper, we present an AR-assisted navigation system for liver resection based on a rigid stereoscopic laparoscope. The stereo image pairs from the laparoscope are used by an unsupervised convolutional network (CNN) framework to estimate depth and generate an intraoperative 3D liver surface. Meanwhile, 3D models of the patient's surgical field are segmented from preoperative CT images using V-Net architecture for volumetric image data in an end-to-end predictive style. A globally optimal iterative closest point (Go-ICP) algorithm is adopted to register the pre- and intraoperative models into a unified coordinate space; then, the preoperative 3D models are superimposed on the live laparoscopic images to provide the surgeon with detailed information about the subsurface of the patient's anatomy, including tumors, their resection margins and vessels.

RESULTS

The proposed navigation system is tested on four laboratory ex vivo porcine livers and five operating theatre in vivo porcine experiments to validate its accuracy. The ex vivo and in vivo reprojection errors (RPE) are 6.04 ± 1.85 mm and 8.73 ± 2.43 mm, respectively.

CONCLUSION AND SIGNIFICANCE

Both the qualitative and quantitative results indicate that our AR-assisted navigation system shows promise and has the potential to be highly useful in clinical practice.

Elevated aerobic glycolysis in renal tubular epithelial cells influences the proliferation and differentiation of podocytes and promotes renal interstitial fibrosis

OBJECTIVE

The aim of this study was to elaborate the influence of changing energy metabolism pattern of renal tubular epithelial cells in the process of renal interstitial fibrosis on podocytes. Meanwhile, we also investigated the relationship between energy metabolism pattern and the development of renal interstitial fibrosis.

MATERIALS AND METHODS

We established a model of renal interstitial fibrosis by unilateral ureteral obstruction (UUO). The protein and messenger RNA (mRNA) expression of fibrosis signs, such as α-smooth muscle actin (α-SMA) and fibronectin (FN) were detected. We also measured the protein and mRNA expression of key glycolytic enzymes, including pyruvate kinase muscle isozyme 2 (PKM2) and human glandular kallikrein 2 (HK2). The proliferation and differentiation of podocytes during fibrosis were observed by monitoring the expression of nephrin and myocardin. In vitro experiments, primary podocytes were extracted, cultured, and stimulated with lactate. Then the alterations during the process were observed. Finally, PKM2 expression was inhibited by intravenous infusion of the plasmid. The link between the expression of marker protein as well as differentiation protein in podocytes and renal interstitial fibrosis was analyzed.

RESULTS

During the process of renal interstitial fibrosis, phenotypic changes and enhanced expression of fibrosis and proliferation markers were found in fibroblasts. Meanwhile, in renal tubular epithelial cells, increased expression of key enzymes of glycolysis, the level of glycolysis as well as lactate metabolites cooperatively led to hypoxic and acidic environment, eventually inhibiting the proliferation and differentiation of podocytes and aggravating fibrosis. When the level of glycolysis in renal tubular epithelial cells was reduced, the number and function of podocytes were partially restored, and renal interstitial fibrosis was alleviated.

CONCLUSIONS

During renal interstitial fibrosis, glycolysis of renal tubular epithelial cell was increased, leading to the recodification of energy metabolism. This process affected the number and function of podocytes and aggravated renal interstitial fibrosis.

Details preserved unsupervised depth estimation by fusing traditional stereo knowledge from laparoscopic images

Depth estimation plays an important role in vision-based laparoscope surgical navigation systems. Most learning-based depth estimation methods require ground truth depth or disparity images for training; however, these data are difficult to obtain in laparoscopy. The authors present an unsupervised learning depth estimation approach by fusing traditional stereo knowledge. The traditional stereo method is used to generate proxy disparity labels, in which unreliable depth measurements are removed via a confidence measure to improve stereo accuracy. The disparity images are generated by training a dual encoder-decoder convolutional neural network from rectified stereo images coupled with proxy labels generated by the traditional stereo method. A principled mask is computed to exclude the pixels, which are not seen in one of views due to parallax effects from the calculation of loss function. Moreover, the neighbourhood smoothness term is employed to constrain neighbouring pixels with similar appearances to generate a smooth depth surface. This approach can make the depth of the projected point cloud closer to the real surgical site and preserve realistic details. The authors demonstrate the performance of the method by training and evaluation with a partial nephrectomy da Vinci surgery dataset and heart phantom data from the Hamlyn Centre.

Delineating functional segregations of the human middle temporal gyrus with resting-state functional connectivity and coactivation patterns

Although the middle temporal gyrus (MTG) has been parcellated into subregions with distinguished anatomical connectivity patterns, whether the structural topography of MTG can inform functional segregations of this area remains largely unknown. Accumulating evidence suggests that the brain's underlying organization and function can be directly and effectively delineated with resting‐state functional connectivity (RSFC) by identifying putative functional boundaries between cortical areas. Here, RSFC profiles were used to explore functional segregations of the MTG and defined four subregions from anterior to posterior in two independent datasets, which showed a similar pattern with MTG parcellation scheme obtained using anatomical connectivity. The functional segregations of MTG were further supported by whole brain RSFC, coactivation, and specific RFSC, and coactivation mapping. Furthermore, the fingerprint with predefined 10 networks and functional characterizations of each subregion using meta‐analysis also identified functional distinction between subregions. The specific connectivity analysis and functional characterization indicated that the bilateral most anterior subregions mainly participated in social cognition and semantic processing; the ventral middle subregions were involved in social cognition in left hemisphere and auditory processing in right hemisphere; the bilateral ventro‐posterior subregions participated in action observation, whereas the left subregion was also involved in semantic processing; both of the dorsal subregions in superior temporal sulcus were involved in language, social cognition, and auditory processing. Taken together, our findings demonstrated MTG sharing similar structural and functional topographies and provide more detailed information about the functional organization of the MTG, which may facilitate future clinical and cognitive research on this area.

Retraction: Randomized controlled trial of vitamin D supplementation in children with autism spectrum disorder

The above article, published in print in the Jan 2018 issue of the Journal of Child Psychology & Psychiatry and online in Wiley Online Library (wileyonlinelibrary.com), has been retracted by the JCPP Editor-in-Chief, Edmund Sonuga-Barke, and John Wiley & Sons. Following a series of communications from readers highlighting concerns about the paper (now published on the journal website), the journal editors requested that the authors send them the raw data from the trial. In response the authors informed the editors that; (i) the electronic data base had been lost following a computer outage and (ii) that they could send only 95 out of 120 hard-copy participant data sheets as one site had closed and was no longer contactable. The substantial data loss in and of itself posed a serious difficulty in verifying the correctness of the data presented in the paper. The JCPP then analysed the data from the 95 cases itself. A number of significant discrepancies emerged between the re-analysis and the findings reported in the paper both in terms of means and standard deviations of key outcome variables across the trial. These involved very substantial differences that we judged to be extremely unlikely to have arisen due to variations in composition of the original and re-analysed samples. We also discovered previously unidentified/reported problems with missing data and recording irregularities regarding changes in treatment regimen and subject identifiers. As a result of these issues the Editors no longer have confidence in the findings reported in the original paper. Based on all these matters combined and following published guidance from the Committee on Publishing Ethics (COPE) and Wiley's Best Practice Guidelines on Publishing Ethics, we have decided that the only course of action available to us is to retract the paper.

Unsupervised binocular depth prediction network for laparoscopic surgery

Minimally invasive surgery (MIS) is characterized by less trauma, shorter recovery time, and lower postoperative infection rate. The two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting precise and complex surgical operations. Three-dimensional (3D) laparoscopic imaging provides surgeons depth perception. This study aims to 3D reconstruction of the surgical scene based on the disparity map generated by the depth estimation algorithm. An unsupervised learning autoencoder method was proposed to calculate the accurate disparity with a 101-layer residual convolutional network. The loss function included three parts: left-right consistency loss, structure similarity loss, and reconstruction error loss, the combination can improve reconstruction accuracy and robustness. The method was validated on a Hamlyn Center Laparoscopic/Endoscopic Video Dataset. The structural similarity index (SSIM) is 0.8349 ± 0.0523 and the peak signal-to-noise ratio (PSNR) is 14.4957 ± 1.9676. The depth prediction network has high accuracy and robustness. The average time to produce each disparity map is about 16 ms. The experimental result shows that the proposed depth estimation method can offer dense disparity map, and can meet surgical real-time requirement. Future work will focus on network structure optimization and loss function design, transfer learning to improve the robustness and accuracy further.

Unsupervised binocular depth prediction network for laparoscopic surgery

Minimally invasive laparoscopic surgery is associated with small wounds and short recovery time, reducing postoperative infections. Traditional two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting the field of vision and operation during surgery. However, three-dimensional (3D) laparoscopic imaging from 2 D images lets surgeons have a depth perception. However, the depth information is not quantitative and cannot be used for robotic surgery. Therefore, this study aimed to reconstruct the accurate depth map for binocular 3 D laparoscopy. In this study, an unsupervised learning method was proposed to calculate the accurate depth while the ground-truth depth was not available. Experimental results proved that the method not only generated accurate depth maps but also provided real-time computation, and it could be used in minimally invasive robotic surgery.

A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy

Objective

The objective of the study was to develop and validate a radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula (POPF) in patients undergoing pancreaticoduodenectomy (PD).

Materials and methods

A total of 117 consecutive patients who underwent PD were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography of the above patients. The least absolute shrinkage and selection operator logistic regression was used to construct a formula of Rad-score calculation. Then the performance of the formula was assessed with standard pancreatic Fistula Risk Score.

Results

The Rad-score could predict POPF with an area under the curve (AUC) of 0.8248 in the training cohort and of 0.7609 in the validation cohort. Patients who had experienced POPF generally had a statistically higher Rad-score than those who had not experienced POPF in both cohorts. The AUC of the Rad-score was statistically higher than the Fistula Risk Score for predicting POPF in both the training and validation cohort.

Conclusion

A novel radiomics-based formula was developed and validated for predicting POPF in patients who underwent PD, which provides a new method for identifying POPF risks and may help to improve informed decision-making in the prevention of POPF at low cost.

A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma

OBJECTIVES

To develop and validate a radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC).

METHODS

One hundred twelve consecutive HCC patients who underwent hepatectomy were included in the study pool (training cohort: n = 80, validation cohort: n = 32), and another 13 patients were included in a pilot prospective analysis. A total of 713 radiomics features were extracted from portal-phase computed tomography (CT) images. A logistic regression was used to construct a radiomics score (Rad-score). Then a nomogram, including Rad-score and other risk factors, was built with a multivariate logistic regression model. The discrimination, calibration and clinical utility of nomogram were evaluated.

RESULTS

The Rad-score could predict PHLF with an AUC of 0.822 (95% CI, 0.726-0.917) in the training cohort and of 0.762 (95% CI, 0.576-0.948) in the validation cohort; however, the approach could not completely outmatch the existing methods (CP [Child-Pugh], MELD [Model of End Stage Liver Disease], ALBI [albumin-bilirubin]). The individual predictive nomogram that included the Rad-score, MELD and performance status (PS) showed better discrimination with an AUC of 0.864 (95% CI, 0.786-0.942), which was higher than the AUCs of the conventional methods (nomogram vs CP, MELD, and ALBI at P < 0.001, P < 0.005, and P < 0.005, respectively). In the validation cohort, the nomogram discrimination was also superior to those of the other three methods (AUC: 0.896; 95% CI, 0.774-1.000). The calibration curves showed good agreement in both cohorts, and the decision curve analysis of the entire cohort revealed that the nomogram was clinically useful. A pilot prospective analysis showed that the radiomics nomogram could predict PHLF with an AUC of 0.833 (95% CI, 0.591-1.000).

CONCLUSIONS

A nomogram based on the Rad-score, MELD, and PS can predict PHLF.