Kidney and Tumor Segmentation Based on 3D Context Extracting

Organ segmentation and lesion detection play a vital role in the computer-aided diagnosis (CAD) systems. The task of this Kits challenge is about kidney and tumor segmentation. We proposed an effective model to complete this Kits challenge. Our model receives part of body 3D scans as input, and outputs the probability map of the input scans. 2D contexts of intra-slices are extracted by VGG network, and 3D contexts of inter-slices are presented by concatenating the 2D contexts. Then proposals are extracted by region proposal network (RPN), while 3D context are regarded as auxiliary information for region of interest (ROI) regression, classification and mask generation. Our model has shown promising result for this Kits challenge

Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications