Related papers: DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images

DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images

URL: http://arxiv.org/abs/2006.06278v3
Date: Sun, 20 Dec 2020 03:15:56 GMT
Title: DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images
Authors: Pin Tang, Chen Zu, Mei Hong, Rui Yan, Xingchen Peng, Jianghong Xiao, Xi Wu, Jiliu Zhou, Luping Zhou, and Yan Wang
Abstract summary: We propose a Dense SegU-net (DSU-net) framework for automatic nasopharyngeal carcinoma (NPC) segmentation in MRI. To combat the potential vanishing-gradient problem, we introduce dense blocks which can facilitate feature propagation and reuse. Our proposed architecture outperforms the existing state-of-the-art segmentation networks.
Score: 30.747375849126925
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Precise and accurate segmentation of the most common head-and-neck tumor, nasopharyngeal carcinoma (NPC), in MRI sheds light on treatment and regulatory decisions making. However, the large variations in the lesion size and shape of NPC, boundary ambiguity, as well as the limited available annotated samples conspire NPC segmentation in MRI towards a challenging task. In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI. Our contribution is threefold. First, different from the traditional decoder in U-net using upconvolution for upsamling, we argue that the restoration from low resolution features to high resolution output should be capable of preserving information significant for precise boundary localization. Hence, we use unpooling to unsample and propose SegU-net. Second, to combat the potential vanishing-gradient problem, we introduce dense blocks which can facilitate feature propagation and reuse. Third, using only cross entropy (CE) as loss function may bring about troubles such as miss-prediction, therefore we propose to use a loss function comprised of both CE loss and Dice loss to train the network. Quantitative and qualitative comparisons are carried out extensively on in-house datasets, the experimental results show that our proposed architecture outperforms the existing state-of-the-art segmentation networks.

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