AutoComb: Automated Comb Sign Detector for 3D CTE Scans

Comb Sign is an important imaging biomarker to detect multiple gastrointestinal diseases. It shows up as increased blood flow along the intestinal wall indicating potential abnormality, which helps doctors diagnose inflammatory conditions. Despite its clinical significance, current detection methods are manual, time-intensive, and prone to subjective interpretation due to the need for multi-planar image-orientation. To the best of our knowledge, we are the first to propose a fully automated technique for the detection of Comb Sign from CTE scans. Our novel approach is based on developing a probabilistic map that shows areas of pathological hypervascularity by identifying fine vascular bifurcations and wall enhancement via processing through stepwise algorithmic modules. These modules include utilising deep learning segmentation model, a Gaussian Mixture Model (GMM), vessel extraction using vesselness filter, iterative probabilistic enhancement of vesselness via neighborhood maximization and a distance-based weighting scheme over the vessels. Experimental results demonstrate that our pipeline effectively identifies Comb Sign, offering an objective, accurate, and reliable tool to enhance diagnostic accuracy in Crohn's disease and related hypervascular conditions where Comb Sign is considered as one of the important biomarkers.

KAN-Mamba FusionNet: Redefining Medical Image Segmentation with Non-Linear Modeling

Medical image segmentation is crucial in robotic surgeries, disease diagnosis, and treatment plans. This research presents an innovative methodology that combines Kolmogorov-Arnold Networks (KAN) with an adapted Mamba layer for medical image segmentation. The proposed KAN-Mamba FusionNet framework improves image segmentation by integrating attention-driven mechanisms with convolutional parallel training and autoregressive deployment, while preserving interpretability, in contrast to the state-of-the-art techniques that depend exclusively on Mamba for ailment localization and accurate diagnosis. We evaluated our proposed KAN-Mamba FusionNet model on three distinct medical image segmentation datasets, BUSI, Kvasir-Seg and GlaS. The results indicated that the KAN-Mamba FusionNet consistently yields better IoU and F1 scores in comparison to the state-of-the-art methods. Further, we offer insights into the model's behavior via ablation studies, examining the effects of various components and assessing their contributions to the overall performance of the proposed model. The findings illustrate the strength and effectiveness of this methodology for dependable medical image segmentation, providing a unique approach to address intricate visual data issues in healthcare.

Bridging the Diagnostic Divide: Classical Computer Vision and Advanced AI methods for distinguishing ITB and CD through CTE Scans

Differentiating between Intestinal Tuberculosis (ITB) and Crohn's Disease (CD) poses a significant clinical challenge due to their similar symptoms, clinical presentations, and imaging features. This study leverages Computed Tomography Enterography (CTE) scans, deep learning, and traditional computer vision to address this diagnostic dilemma. A consensus among radiologists from renowned institutions has recognized the visceral-to-subcutaneous fat (VF/SF) ratio as a surrogate biomarker for differentiating between ITB and CD. Previously done manually, we propose a novel 2D image computer vision algorithm for auto-segmenting subcutaneous fat to automate this ratio calculation, enhancing diagnostic efficiency and objectivity. As a benchmark, we compare the results to those obtained using the TotalSegmentator tool, a popular deep learning-based software for automatic segmentation of anatomical structures, and manual calculations by radiologists. We also demonstrated the performance on 3D CT volumes using a slicing method and provided a benchmark comparison of the algorithm with the TotalSegmentator tool. Additionally, we propose a scoring approach to integrate scores from radiological features, such as the fat ratio and pulmonary TB probability, into a single score for diagnosis. We trained a ResNet10 model on a dataset of CTE scans with samples from ITB, CD, and normal patients, achieving an accuracy of 75%. To enhance interpretability and gain clinical trust, we integrated the explainable AI technique Grad-CAM with ResNet10 to explain the model's predictions. Due to the small dataset size (100 total cases), the feature-based scoring system is considered more reliable and trusted by radiologists compared to the deep learning model for disease diagnosis.