Hierarchical 3D Feature Learning for Pancreas Segmentation

Federica Proietto Salanitri; Giovanni Bellitto; Ismail Irmakci; Simone Palazzo; Ulas Bagci; Concetto Spampinato

doi:10.1007/978-3-030-87589-3_25

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arXiv · 2109.01667

Hierarchical 3D Feature Learning for Pancreas Segmentation

Federica Proietto Salanitri·Giovanni Bellitto·Ismail Irmakci·Simone Palazzo·Ulas Bagci·Concetto Spampinato

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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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Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci, Simone Palazzo, Ulas Bagci, Concetto Spampinato. 2021-09-03. Hierarchical 3D Feature Learning for Pancreas Segmentation. https://doi.org/10.1007/978-3-030-87589-3_25

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