Hierarchical 3D Feature Learning forPancreas Segmentation

Küçük Resim Yok

Tarih

2021

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Science and Business Media Deutschland GmbH

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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. © 2021, Springer Nature Switzerland AG.

Açıklama

12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 -- 27 September 2021 through 27 September 2021 -- -- 266089

Anahtar Kelimeler

CT and MRI pancreas segmentation, Fully convolutional neural networks, Hierarchical encoder-decoder architecture, 3D modeling, Computerized tomography, Convolution, Convolutional neural networks, Decoding, Machine learning, Medical computing, Medical imaging, Network coding, Statistical tests, Convolutional neural network, CT and MRI pancreas segmentation, CT-scan, Encoder-decoder architecture, Feature learning, Fully convolutional neural network, Hierarchical encoder-decoder architecture, Learn+, MRI scan, Segmentation map, Magnetic resonance imaging

Kaynak

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

WoS Q Değeri

Scopus Q Değeri

Q3

Cilt

12966 LNCS

Sayı

Künye