Hierarchical 3D Feature Learning for Pancreas Segmentation

• URL: http://arxiv.org/abs/2109.01667v1
• Date: Fri, 3 Sep 2021 09:27:07 GMT
• Title: Hierarchical 3D Feature Learning for Pancreas Segmentation
• Authors: Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci, Simone Palazzo, Ulas Bagci, Concetto Spampinato
• Abstract summary: We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. Our model outperforms existing methods on CT pancreas segmentation, obtaining an average Dice score of about 88%. Additional control experiments demonstrate that the achieved performance is due to the combination of our 3D fully-convolutional deep network and the hierarchical representation decoding.
• Score: 11.588903060674344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to multiple 3D decoders that individually predict intermediate segmentation maps. Finally, all segmentation maps are combined to obtain a unique detailed segmentation mask. We test our model on both CT and MRI imaging data: the publicly available NIH Pancreas-CT dataset (consisting of 82 contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans). Experimental results show that our model outperforms existing methods on CT pancreas segmentation, obtaining an average Dice score of about 88%, and yields promising segmentation performance on a very challenging MRI data set (average Dice score is about 77%). Additional control experiments demonstrate that the achieved performance is due to the combination of our 3D fully-convolutional deep network and the hierarchical representation decoding, thus substantiating our architectural design.

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