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

A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation

In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial infor- mation along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-S{\o}rensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.Comment: 9 pages, 4 figures, Accepted to 3D

Towards Robust Deep Learning for Medical Image Analysis

Multi-dimensional medical data are rapidly collected to enhance healthcare. With the recent advance in artificial intelligence, deep learning techniques have been widely applied to medical images, constituting a significant proportion of medical data. The techniques of automated medical image analysis have the potential to benefit general clinical procedures, e.g., disease screening, malignancy diagnosis, patient risk prediction, and surgical planning. Although preliminary success takes place, the robustness of these approaches requires to be cautiously validated and sufficiently guaranteed before their application to real-world clinical problems. In this thesis, we propose different approaches to improve the robustness of deep learning algorithms for automated medical image analysis. (i) In terms of network architecture, we leverage the advantages of both 2D and 3D networks, and propose an alternative 2.5D approach for 3D organ segmentation. (ii) To improve data efficiency and utilize large-scale unlabeled medical data, we propose a unified framework for semi-supervised medical image segmentation and domain adaptation. (iii) For the safety-critical applications, we design a unified approach for failure detection and anomaly segmentation. (iv) We study the problem of Federated Learning, which enables collaborative learning and preserves data privacy, and improve the robustness of the algorithm in the non-i.i.d setting. (v) We incorporate multi-phase information for more accurate pancreatic tumor detection. (vi) Finally, we show our discovery for potential pancreatic cancer screening on non-contrast CT scans which outperform expert radiologists