Automated pancreas segmentation and volumetry using deep neural network on computed tomography

The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics

Laryngeal high-speed video (HSV)-endoscopy allows for fast, non-invasive diagnosis of voice disorders and forms the basis for a comprehensive quantitative analysis of the vocal folds' (VFs') spatiotemporal vibrational behavior. Previous approaches, such as the Phonovibrogram (PVG), describe the vibrational behavior of vocal folds (VFs) based exclusively on the time-varying glottal opening. However, focusing solely on the glottal area overlooks the full extent and dynamic behavior of the VF tissue, factors that are crucial for the voice production process. This complicates clinical interpretation and, thus, the comparability of vibrational dynamics in both cross-sectional and longitudinal interventional studies. To address these limitations, this work aims to extend the PVG to provide a more comprehensive representation of the vibrational behavior across the entire VF tissue. Here, we present the Laryngovibrogram (LVG), which is obtained by segmenting not only the glottal area but also the VFs' tissue, providing a compact quantitative representation of the VFs' vibrational behavior. The potential of the proposed LVG representation was investigated on 73 HSV recordings from healthy (38 HSVs) and pathological subjects (35 HSVs) in stationary as well as non-stationary phonations. It is demonstrated that the LVG reliably maps the vibrational behavior along the entire length of the VFs tissue for both physiological and pathological phonations. Compared to PVG-based measures, LVG-based measures exhibited greater stability in healthy subjects, allowing for a narrower normative range, and showed stronger effect sizes in differentiating clinical groups, suggesting a more robust assessment of vibratory impairments. By scaling the vibration amplitude relative to the length of the segmented VF tissue, the VF vibrations are normalized, enabling meaningful quantitative intra- and inter-individual comparisons. Additionally, calculating the angle enclosed by the two VFs makes it possible to analyze transient effects that occur during non-stationary phonation maneuvers, such as voice onset. By integrating information about the VF tissue, the LVG introduced here represents a paradigm shift in the analysis of laryngeal dynamics from focusing solely on the glottal area to a holistic analysis of the entire VF kinematics, which might improve pathology detection accuracy, reduce subjective assessment errors, and optimize treatment follow-ups, ultimately enhancing both clinical diagnostics and therapeutic outcomes.

Pancreatic atrophy after gastric cancer surgery: influencing factors and effects on BMI and quality of life

BACKGROUND

Pancreatic atrophy can occur after gastric cancer surgery, but the influencing factors and effects of pancreatic atrophy have not been extensively studied. The aim of this study was to investigate the factors of pancreatic atrophy after gastric cancer surgery and to assess the effect of atrophy on BMI and quality of life, in order to promote postoperative management of patients with higher risk factors of pancreatic atrophy.

METHODS

Clinical data pertaining to 142 patients who underwent surgery for gastric cancer were retrospectively collected, and pancreatic volume was determined using abdominal computed tomography data. Influencing factors of pancreatic atrophy were analysed and the relationship of pancreatic atrophy to BMI and quality of life was measured. Correlation analysis using Pearson or Spearman rank correlation and multiple linear regression were used to analyse the risk factors influencing pancreatic atrophy.

RESULTS

Pancreatic atrophy was significant in patients with gastric cancer 1 year after surgery, regardless of the surgical procedure. T3 and T4 stages, preoperative low levels of high-density lipoprotein cholesterol(HDL-C) and smoking history were influencing factors of pancreatic atrophy. Pancreatic atrophy was associated with reduced BMI and deterioration of quality of life.

CONCLUSIONS

Clinicians need to monitor pancreatic function, BMI and life quality more carefully in gastric cancer patients with T3 and T4 stages, preoperative low levels of HDL-C and smoking history.

Unsupervised cross-modality domain adaptation via source-domain labels guided contrastive learning for medical image segmentation

Unsupervised domain adaptation (UDA) offers a promising approach to enhance discriminant performance on target domains by utilizing domain adaptation techniques. These techniques enable models to leverage knowledge from the source domain to adjust to the feature distribution in the target domain. This paper proposes a unified domain adaptation framework to carry out cross-modality medical image segmentation from two perspectives: image and feature. To achieve image alignment, the loss function of Fourier-based Contrastive Style Augmentation (FCSA) has been fine-tuned to increase the impact of style change for improving system robustness. For feature alignment, a module called Source-domain Labels Guided Contrastive Learning (SLGCL) has been designed to encourage the target domain to align features of different classes with those in the source domain. In addition, a generative adversarial network has been incorporated to ensure consistency in spatial layout and local context in generated image space. According to our knowledge, our method is the first attempt to utilize source domain class intensity information to guide target domain class intensity information for feature alignment in an unsupervised domain adaptation setting. Extensive experiments conducted on a public whole heart image segmentation task demonstrate that our proposed method outperforms state-of-the-art UDA methods for medical image segmentation.

Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis

Background

Acute pancreatitis (AP) is a potentially life-threatening condition characterized by inflammation of the pancreas, which can lead to complications such as pancreatic necrosis. The modified computed tomography severity index (MCTSI) is a widely used tool for assessing the severity of AP, particularly the extent of pancreatic necrosis. The accurate and timely assessment of the necrosis volume is crucial in guiding treatment decisions and improving patient outcomes. However, the current diagnostic process relies heavily on the manual interpretation of computed tomography (CT) scans, which can be subjective and prone to variability among clinicians. This study aimed to develop a deep-learning network model to assist clinicians in diagnosing the volume ratio of pancreatic necrosis based on the MCTSI for AP.

Methods

The datasets comprised retrospectively collected plain and contrast-enhanced CT scans from 144 patients (6 with scores of 0 points, 42 with scores of 2 points, and 65 with scores of 4 points) and the National Institutes of Health contrast-enhanced CT scans from 45 patients with scores of 0 points. An improved fully convolutional neural networks for volumetric medical image segmentation (V-Net) model was developed to segment the pancreatic volume (i.e., the whole pancreas, necrotic pancreatic tissue, and non-necrotic pancreatic tissue) and to quantify the split volume ratios. The improved strategy included three stages of body up- and down-sampling adapted to the task of segmentation in AP, and the selection of objects, loss function, and smoothing coefficients. The model interpretations were compared with those of clinicians with different levels of experience. The reference standard was manually segmented by a pancreatic radiologist. Accuracy, macro recall, and macro specificity were employed to compare the diagnostic efficacy of the model and the clinicians.

Results

In total, 144 patients (mean age: 44±13 years; 40 females, 104 males) were included in the study. Optimal training results were obtained using the necrotic pancreatic tissue and whole pancreas as the input objects, and combining dice loss and 500 smoothing coefficients as the loss function for training. The dice coefficient for the whole pancreas was 0.811 and that for the necrotic pancreatic tissue was 0.761. The performance of the artificial intelligence model and clinicians were compared. The accuracy, macro recall, and macro specificity of the improved V-net were 0.854, 0.850 and 0.923, respectively, which were all significantly higher than those of the senior and junior clinicians (P<0.05).

Conclusions

Our proposed model could improve the effectiveness of clinicians in diagnosing pancreatic necrosis volume ratios in clinical settings.

Large-scale multi-center CT and MRI segmentation of pancreas with deep learning

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R2 of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.

AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.

Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.

DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas

The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable potential, especially in optimizing radiological evaluations. However, training DL algorithms to clinical standards requires extensive datasets, and their processing is labor-intensive. In this study, we developed an annotation tool named DLLabelsCT that utilizes CNN models to accelerate the image analysis process. To validate DLLabelsCT, we trained a CNN model with a ResNet34 encoder and a UNet decoder to segment the pancreas on an open-access dataset and used the DL model to assist in annotating a local dataset, which was further used to refine the model. DLLabelsCT was also tested on two external testing datasets. The tool accelerates annotation by 3.4 times compared to a completely manual annotation method. Out of 3,715 CT scan slices in the testing datasets, 50% did not require editing when reviewing the segmentations made by the ResNet34-UNet model, and the mean and standard deviation of the Dice similarity coefficient was 0.82±0.24. DLLabelsCT is highly accurate and significantly saves time and resources. Furthermore, it can be easily modified to support other deep learning models for other organs, making it an efficient tool for future research involving larger datasets.

Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation

BACKGROUND

The pancreas is a complex abdominal organ with many anatomical variations, and therefore automated pancreas segmentation from medical images is a challenging application.

PURPOSE

In this paper, we present a framework for segmenting individual pancreatic subregions and the pancreatic duct from three-dimensional (3D) computed tomography (CT) images.

METHODS

A multiagent reinforcement learning (RL) network was used to detect landmarks of the head, neck, body, and tail of the pancreas, and landmarks along the pancreatic duct in a selected target CT image. Using the landmark detection results, an atlas of pancreases was nonrigidly registered to the target image, resulting in anatomical probability maps for the pancreatic subregions and duct. The probability maps were augmented with multilabel 3D U-Net architectures to obtain the final segmentation results.

RESULTS

To evaluate the performance of our proposed framework, we computed the Dice similarity coefficient (DSC) between the predicted and ground truth manual segmentations on a database of 82 CT images with manually segmented pancreatic subregions and 37 CT images with manually segmented pancreatic ducts. For the four pancreatic subregions, the mean DSC improved from 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shifted windowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, when utilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperforming the standard approaches and existing methods on different datasets.

CONCLUSIONS

The resulting accuracy of the proposed RL-based segmentation framework demonstrates an improvement against segmentation with standard U-Net architectures.

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality

Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications.

Pancreatic volume and endocrine function changes following pancreaticoduodenectomy for peri-ampullary neoplasms: A retrospective single-center study utilizing pancreas volumetry

Backgrounds/Aims

We evaluated long-term pancreatic functional outcomes, including pancreatic volumetry after pancreaticoduodenectomy (PD) for peri-ampullary neoplasm.

Methods

We retrospectively reviewed 353 patients with a 12-month follow-up who underwent elective pancreaticoduodenectomies for peri-ampullary neoplasms at a single university hospital between January 2011 and December 2020. Perioperative and postoperative outcomes, long-term pancreatic endocrine functions, and pancreatic volume changes 12 month postoperatively were evaluated.

Results

The mean age was 65.4 years, and the sex ratio was 1.38. The patients with prediagnosed diabetes mellitus (DM) comprised 31.4%. The peri-ampullary neoplasm origins were: the pancreas (49.0%), common bile duct (27.2%), ampulla of Vater (18.4%), and duodenum (5.4%). The 1-week, and 3-, 6-, and 12-month postoperative proportions of patients with DM diagnosed before surgery combined with new-onset postoperative DM were 39.7%, 42.8%, 43.9%, and 49.6%, respectively. The preoperative and postoperative 1-week, and 3-, 6-, and 12-month mean pancreatic volumes were 82.3, 38.7, 28.1, 24.9, and 25.5 mL, respectively. Univariate risk factor analyses for new-onset DM after PD observed no significant difference between the 'No DM after PD' and 'New-onset DM after PD' groups.

Conclusions

Following PD for peri-ampullary neoplasms, pancreatic endocrine functions and volumes continued to decrease for a minimum of 12 months. The current study did not identify any causal relationship between pancreatic endocrine dysfunction and pancreatic atrophy following PD.

Pancreas volumes in pediatric patients following index acute pancreatitis and acute recurrent pancreatitis

BACKGROUND/OBJECTIVES

Pancreas volume derived from imaging may objectively reveal volume loss relevant to identifying sequelae of acute pancreatitis (AP) and ultimately diagnosing chronic pancreatitis (CP). The purposes of this study were to: (1) quantify pancreas volume by imaging in children with either (a) a single episode of AP or (b) acute recurrent pancreatitis (ARP), and (2) compare these volumes to normative volumes.

METHODS

This retrospective study was institutional review board approved. A single observer segmented the pancreas (3D Slicer; slicer.org) on n = 30 CT and MRI exams for 23 children selected from a prospective registry of patients with either an index attack of AP or with ARP after a known index attack date. Patients with CP were excluded. Segmented pancreas volumes were compared to published normal values.

RESULTS

Mean pancreas volumes normalized to body surface area (BSA) in the index AP and ARP groups were 38.2 mL/m2 (range: 11.8-73.5 mL/m2) and 27.9 mL/m2 (range: 8.0-69.2 mL/m2) respectively. 43 % (6/14) of patients post-AP had volumes below the 25th percentile, 1 (17 %) of which was below the 5th percentile (p = 0.3027 vs. a normal distribution). Post-ARP, 44 % (7/16) of patients had volumes below the 5th percentile (p < 0.001).

CONCLUSIONS

A significant fraction (40 %) of children with ARP have pancreas volumes <5th percentile for BSA even in the absence of CP. A similar, but not statistically significant, fraction have pancreas volumes <25th percentile after an index attack of AP. Pancreatic parenchymal volume deserves additional investigation as an objective marker of parenchymal damage from acute pancreatitis and of progressive pancreatitis in children.

Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy

Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65-75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.

Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks

This study aimed to evaluate acute pancreatitis (AP) severity using convolutional neural network (CNN) models with enhanced computed tomography (CT) scans. Three-dimensional DenseNet CNN models were developed and trained using the enhanced CT scans labeled with two severity assessment methods: the computed tomography severity index (CTSI) and Atlanta classification. Each labeling method was used independently for model training and validation. Model performance was evaluated using confusion matrices, areas under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, and respective macro-average metrics. A total of 1,798 enhanced CT scans met the inclusion criteria were included in this study. The dataset was randomly divided into a training dataset (n = 1618) and a test dataset (n = 180) with a ratio of 9:1. The DenseNet model demonstrated promising predictions for both CTSI and Atlanta classification-labeled CT scans, with accuracy greater than 0.7 and AUC-ROC greater than 0.8. Specifically, when trained with CT scans labeled using CTSI, the DenseNet model achieved good performance, with a macro-average F1 score of 0.835 and a macro-average AUC-ROC of 0.980. The findings of this study affirm the feasibility of employing CNN models to predict the severity of AP using enhanced CT scans.

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

Artificial intelligence in pancreatic cancer

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.