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Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks

arXiv 2302.06356 ↗First posted 13 Feb 2023cs.CVcited by 0

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Abstract (arXiv)

Pancreatic cancer is one of the deadliest types of cancer, with 25% of the diagnosed patients surviving for only one year and 6% of them for five. Computed tomography (CT) screening trials have played a key role in improving early detection of pancreatic cancer, which has shown significant improvement in patient survival rates. However, advanced analysis of such images often requires manual segmentation of the pancreas, which is a time-consuming task. Moreover, pancreas presents high variability in shape, while occupying only a very small area of the entire abdominal CT scans, which increases the complexity of the problem. The rapid development of deep learning can contribute to offering robust algorithms that provide inexpensive, accurate, and user-independent segmentation results that can guide the domain experts. This dissertation addresses this task by investigating a two-step approach for pancreas segmentation, by assisting the task with a prior rough localization or detection of pancreas. This rough localization of the pancreas is provided by an estimated probability map and the detection task is achieved by using the YOLOv4 deep learning algorithm. The segmentation task is tackled by a modified U-Net model applied on cropped data, as well as by using a morphological active contours algorithm. For comparison, the U-Net model was also applied on the full CT images, which provide a coarse pancreas segmentation to serve as reference. Experimental results of the detection network on the National Institutes of Health (NIH) dataset and the pancreas tumour task dataset within the Medical Segmentation Decathlon show 50.67% mean Average Precision. The best segmentation network achieved good segmentation results on the NIH dataset, reaching 67.67% Dice score.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 53 references that are in the map. First posted on arXiv 13 Feb 2023.

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References · 20

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8 more
  1. 2017Hierarchical 3D fully convolutional networks for multi-organ segmentationcited by 19
  2. 2017Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentationcited by 18
  3. 2019Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Netcited by 15
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