Software for hepatic vessel classification: feasibility study for virtual surgery

SCAN: sequence-based context-aware association network for hepatic vessel segmentation

Accurate segmentation of hepatic vessel is significant for the surgeons to design the preoperative planning of liver surgery. In this paper, a sequence-based context-aware association network (SCAN) is designed for hepatic vessel segmentation, in which three schemes are incorporated to simultaneously extract the 2D features of hepatic vessels and capture the correlations between adjacent CT slices. The two schemes of slice-level attention module and graph association module are designed to bridge feature gaps between the encoder and the decoder in the low- and high-dimensional spaces. The region-edge constrained loss is designed to well optimize the proposed SCAN, which integrates cross-entropy loss, dice loss, and edge-constrained loss. Experimental results indicate that the proposed SCAN is superior to several existing deep learning frameworks, in terms of 0.845 DSC, 0.856 precision, 0.866 sensitivity, and 0.861 F1-score.

Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention

PURPOSE

Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused a broad range of interest in the medical image analysis community. Due to the complex structure and low-contrast background, automatic liver vessel segmentation remains particularly challenging. Most of the related researches adopt FCN, U-net, and V-net variants as a backbone. However, these methods mainly focus on capturing multi-scale local features which may produce misclassified voxels due to the convolutional operator's limited locality reception field.

METHODS

We propose a robust end-to-end vessel segmentation network called Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net) by expanding swin transformer to 3D and employing an effective combination of convolution and self-attention. In practice, we introduce voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels and adopt multi-scale convolutional operators to gain local spatial information. On the other hand, we propose the inductive biased multi-head self-attention which learns inductively biased relative positional embedding from initialized absolute position embedding. Based on this, we can gain more reliable queries and key matrices.

RESULTS

We conducted experiments on the 3DIRCADb dataset. The average dice and sensitivity of the four tested cases were 74.8[Formula: see text] and 77.5[Formula: see text], which exceed the results of existing deep learning methods and improved graph cuts method. The Branches Detected(BD)/Tree-length Detected(TD) indexes also proved the global/local feature capture ability better than other methods.

CONCLUSION

The proposed model IBIMHAV-Net provides an automatic, accurate 3D liver vessel segmentation with an interleaved architecture that better utilizes both global and local spatial features in CT volumes. It can be further extended for other clinical data.

Appearance of Focal Nodular Hyperplasia after Chemotherapy in Two Patients during Follow-Up of Colon Carcinoma

Surgical liver resection is a treatment option in patients with resectable colorectal liver metastases. We present two cases of focal nodular hyperplasia (FNH) development after treatment with oxaliplatin during follow-up of colon carcinoma. The first case was a 40-year-old male patient who developed multiple liver lesions suspect for metastatic disease four years after he had undergone laparoscopic right-sided hemicolectomy and adjuvant chemotherapy (capecitabine and oxaliplatin). He underwent a metastasectomy of segments three and four and microwave ablation (MWA) of the lesion in segment one. Pathological analysis demonstrated FNH. The second patient was a 21-year-old woman who presented with multiple liver lesions during follow-up for colon carcinoma. She underwent a laparoscopic right-sided hemicolectomy and was adjuvantly treated with capecitabine and oxaliplatin three years ago. Magnetic resonance imaging (MRI) was performed, and the lesions showed no signs of metastatic disease but were classified as FNH. Therefore, the decision was made to follow up the patient. In conclusion, the development of benign liver lesions could occur during follow-up of colon carcinoma and might be caused by oxaliplatin-induced changes to the liver parenchyma. Hence, it is important to distinguish these from metastatic liver disease.

Pre-operative imaging and post-operative appearance of standard paediatric neurosurgical approaches: a training guide for neuroradiologists

A short-cut narrative review was conducted according to the SANRA guidelines to identify studies describing normal and abnormal postoperative radiological features of the most common paediatric neurosurgical procedures. Rather than focusing on the original pathology addressed by neurosurgical means, this review explored three main areas of operative neurosurgery: ventricular access, supratentorial & infratentorial craniotomies, and posterior fossa/craniocervical junction decompression. A total of twenty-three landmark papers were included for review based on their relevance to address the research question and serve as a practical guide for paediatric neuroradiology trainees and fellows. Accurate in text referencing of the ClinicalTrials.gov identifier, and weblink, has also been provided for all trials discussed in the results section. All the above is complemented by relevant iconography meant to describe a wide range of postoperative changes and early complications. Finally, the review is enriched by a discussion touching upon haemostatic agents, intentionally retained foreign bodies and the future of machine learning for neuroradiology reporting. Overall, the information presented in a systematic fashion will not only help trainees and fellows to deepen these topics and expand their knowledge in preparation for written and oral boards, but will also represent a useful resource for everyone including trained neuroradiologists and neurosurgeons themselves.

Local, semi-automatic, three-dimensional liver reconstruction or external provider? An analysis of performance and time expense

PURPOSE

In hepatobiliary surgery, preoperative three-dimensional reconstruction based on CT or MRI can be provided externally or by local, semi-automatic software. We analyzed the time expense and quality of external versus local three-dimensional reconstructions.

METHODS

Three first-year residents reconstructed data from 20 patients with liver pathologies using a local, semi-automatic, server-based program. Initially, five randomly selected patient datasets were segmented, with the visualization of an established external company available for comparison at all times (learning phase). The other fifteen cases were compared with the external datasets after completing local reconstruction (control phase). Total time expense/case and for specific manual and semi-automated reconstruction steps were recorded. Segmentation quality was analyzed by testing the equivalence for liver and tumor volumes, portal vein sectors, and hepatic vein territories.

RESULTS

The median total reconstruction time was reduced from 2.5 h (learning phase) to 1.5 h (control phase) (- 42%; p < 0.001). Comparing the total and detailed liver volumes (sectors and territories) as well as the tumor volumes in the control phase equivalence was proven. In addition, a highly significant correlation between the external and local analysis was obtained over all analyzed segments with a very high ICC (median [IQR]: 0.98 [0.97; 0.99]; p < 0.01).

CONCLUSION

Local, semi-automatic reconstruction performed by inexperienced residents was feasible with an expert level time expense and the quality of the three-dimensional images was comparable with those from an external provider.

A Rare Cause of Persistent Blood Loss after Continuous Ambulatory Peritoneal Dialysis Catheter Placement

The laparoscopic placement of a continuous ambulatory peritoneal dialysis (CAPD) catheter is a widely used method in patients with end stage renal disease (ESRD). The potential complications of this procedure include perforation of intra-abdominal organs, surgical site infection, peritonitis, catheter migration, catheter blockage, port site herniation, and bleeding. In most cases, bleeding is considered to be an early-onset complication because it mostly occurs within the first seven days after surgery. We report a case of a 68-year-old female patient with a previous history of diabetes mellitus, myelodysplastic syndrome, extensive collateral varices, anaemia, and ESRD due to obstructive uropathy caused by retroperitoneal fibrosis, who presented with persistent blood loss after the laparoscopic placement of a CAPD catheter. Duplex ultrasonography showed that the CAPD catheter was transfixing a superficial epigastric varicose vein, a collateral vein, due to the occlusion of the left external iliac vein. Persistent blood loss after inserting a CAPD catheter without previous imaging of abdominal wall vessels is an indication for further diagnostics.

Which neuroimaging techniques are really needed in Chiari I? A short guide for radiologists and clinicians

PURPOSE

To describe the most appropriate techniques and suggested protocols meant to address the various scenarios that clinicians and pediatric neurosurgeons may face in their day-to-day practice connected with Chiari I.

METHODS

Current literature related to image indications and findings in Chiari I has been reviewed. The authors focused on both standard and advanced techniques for clinical diagnosis and preoperative planning purposes.

DISCUSSION AND CONCLUSION

The complexity of providing neuroimaging guidelines for children investigated for Chiari I lies in defining the most appropriate neuroradiology tool to approach what is in fact a very heterogeneous condition with different etiopathogenetic mechanisms and associated abnormalities. Other variables that may influence the diagnostic strategy include the age of the patient, the presence of additional pathological conditions, the type of presenting symptoms, and the indication for surgical or conservative management. Although the average age at time of diagnosis is 10 years, the initial diagnosis may be done at any age, and the referral for neuroradiology workup may come from general practitioners/pediatricians, orthopedic surgeons, and endocrinologists following various baseline investigations including plain x-rays of skull and spine and/or CT head and/or MRI brain and spine.

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was [Formula: see text] 0.83 and [Formula: see text] 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNNs and anatomical analysis can be used for the accurate segmentation of the PV and potentially integrated into liver radiation therapy planning.

Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach

The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.

Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room

Patient-specific 3D models obtained by the segmentation of volumetric diagnostic images play an increasingly important role in surgical planning. Surgeons use the virtual models reconstructed through segmentation to plan challenging surgeries. Many solutions exist for the different anatomical districts and surgical interventions. The possibility to bring the 3D virtual reconstructions with native radiological images in the operating room is essential for fostering the use of intraoperative planning. To the best of our knowledge, current DICOM viewers are not able to simultaneously connect to the picture archiving and communication system (PACS) and import 3D models generated by external platforms to allow a straight integration in the operating room. A total of 26 DICOM viewers were evaluated: 22 open source and four commercial. Two DICOM viewers can connect to PACS and import segmentations achieved by other applications: Synapse 3D® by Fujifilm and OsiriX by University of Geneva. We developed a software network that converts diffuse visual tool kit (VTK) format 3D model segmentations, obtained by any software platform, to a DICOM format that can be displayed using OsiriX or Synapse 3D. Both OsiriX and Synapse 3D were suitable for our purposes and had comparable performance. Although Synapse 3D loads native images and segmentations faster, the main benefits of OsiriX are its user-friendly loading of elaborated images and it being both free of charge and open source.

Management of Gliomas: Overview of the Latest Technological Advancements and Related Behavioral Drawbacks

The advancements in basic sciences and the availability of sophisticated technological aids to surgical removal of gliomas have led over the last few years to the rise of innovative surgical strategies, the identification of better prognostic/predictive biomolecular factors, and the development of novel drugs and all are meant to profoundly impact the outcome of patients diagnosed with these aggressive tumours. Unfortunately, the treatment protocols available nowadays still confer only a small survival advantage at a potentially high cost in terms of overall well-being. In this review we identified the potential and limits of the most promising research trends in the management of glioma patients, also highlighting the related externalities. Finally, we focused our attention on the imbalance between the technical and behavioral aspects pertinent to this research area, which ultimately represent the two sides of the same coin.

Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images

Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p  <  0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

Radiosurgical options in neuro-oncology: a review on current tenets and future opportunities. Part II: adjuvant radiobiological tools

Stereotactic radiosurgery (SRS) is currently a well-established, minimally invasive treatment for many primary and secondary tumors, especially deep-sited lesions for which traditional neurosurgical procedures were poorly satisfactory or not effective at all. The initial evolution of SRS was cautious, relying on more than 30 years of experimental and clinical work that preceded its introduction into the worldwide medical community. This path enabled a brilliant present, and the continuous pace of technological advancement holds promise for a brighter future. Part II of this review article will cover the impact of multimodal adjuvant technologies on SRS, and their input to the crucial role played by neurosurgeons, radiation oncologists and medical physicists in the management and care of fragile neuro-oncological patients.

Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm

RATIONALE AND OBJECTIVES

The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery.

MATERIALS AND METHODS

A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.

RESULTS

The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively.

CONCLUSIONS

A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections.