Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

Abstract

This paper proposes an intuitive approach to finding pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named segmentation-for-classification (S4C), which classifies a volume by checking if at least a sufficient number of voxels is segmented as the tumor. In order to deal with tumors with different scales, we train volumetric segmentation networks with multi-scale inputs, and test them in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We perform a case study on our dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal. Our approach reports a sensitivity of 94.1% at a specificity of 98.5%, with an average tumor segmentation accuracy of 56.46% over all PDAC cases. * The first two authors equally contributed to the work.

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

“…We also used data augmentation during training. Different from single-phase segmentation which commonly uses rotation and scaling [5,20], virtual sets [15] are also utilized in this work. Even though arterial and venous phase scanning are customized for each patient, the level of enhancement can be different from patients by variation of blood circulation, which causes inter-subject enhancement variations on each phase.…”

“…1) and therefore can be easily neglected by even experienced radiologists. To our best knowledge, the state-of-the-art on this matter is [20], which reports an average Dice of 56.46%. For better detection of PDAC mass, dual-phase pancreas protocol using contrast-enhanced CT imaging, which is comprised of arterial and venous phases with intravenous contrast delay, are recommended.…”

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors

“…With the development of deep-learning frameworks 9 , researchers have been able to construct effective deep encoder-decoder networks 10 for pancreas segmentation, boosting diagnostic accuracy 11 - 14 . Zhu et al reported a multi-scale segmentation method for screening pancreatic ductal adenocarcinoma (PDAC) by checking if a sufficient number of voxels were segmented as tumors 15 . Liu et al segmented the pancreas first, and then classified abnormalities to detect PDAC 16 .…”

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

“…Though this work mainly focuses on segmentation for the pancreas, we can naturally apply the proposed idea to other small organs, e.g., spleen, duodenum and gallbladder, etc, In the future, we will target on error causes that lead to inaccurate segmentation to make our framework more stable, and extend our 3D coarse-to-fine framework to cyst segmentation which can cause cancerous tumors, and the very important tumor segmentation [36] task.…”