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Conference Paper: Hybrid loss guided convolutional networks for whole heart parsing

TitleHybrid loss guided convolutional networks for whole heart parsing
Authors
Issue Date2018
PublisherSpringer.
Citation
8th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2017), Held in Conjunction with MICCAI 2017, Quebec City, Canada, 10-14 September 2017. In Pop, M, Sermesant, M, Jodoin, P, et al. (Eds.), Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers, p. 215-223. Cham, Switzerland: Springer, 2018 How to Cite?
AbstractCT and MR are dominant imaging modalities in cardiovascular inspection. Segmenting the whole heart from CT and MR volumes, and parsing it into distinctive substructures are highly desired in clinic. However, traditional methods tend to be degraded by the large variances of heart and image, and also the high requirement in simultaneously distinguishing several substructures. In this paper, we start with the well-founded Fully Convolutional Network (FCN), and closely couple the FCN with 3D operators, transfer learning and deep supervision mechanism to distill 3D contextual information and attack potential difficulties in training deep neural networks. We then focus on a main concern in our enhanced FCN. As the number of substructures to be distinguished increases, the imbalance among different classes will emerge and bias the training towards major classes and therefore should be tackled seriously. Class-balanced loss function is useful in addressing the problem but at the risk of sacrificing the segmentation details. For a better trade-off, in this paper, we propose a hybrid loss which takes advantage of different kinds of loss functions to guide the training procedure to equally treat all classes, and at the same time preserve boundary details, like the branchy structure of great vessels. We verified our method on the MM-WHS Challenge 2017 datasets, which contain both CT and MR. Our hybrid loss guided model presents superior results in concurrently labeling 7 substructures of heart (ranked as second in CT segmentation Challenge). Our framework is robust and efficient on different modalities and can be extended to other volumetric segmentation tasks.
Persistent Identifierhttp://hdl.handle.net/10722/299568
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; 10663

 

DC FieldValueLanguage
dc.contributor.authorYang, Xin-
dc.contributor.authorBian, Cheng-
dc.contributor.authorYu, Lequan-
dc.contributor.authorNi, Dong-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:41Z-
dc.date.available2021-05-21T03:34:41Z-
dc.date.issued2018-
dc.identifier.citation8th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2017), Held in Conjunction with MICCAI 2017, Quebec City, Canada, 10-14 September 2017. In Pop, M, Sermesant, M, Jodoin, P, et al. (Eds.), Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers, p. 215-223. Cham, Switzerland: Springer, 2018-
dc.identifier.isbn9783319755403-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299568-
dc.description.abstractCT and MR are dominant imaging modalities in cardiovascular inspection. Segmenting the whole heart from CT and MR volumes, and parsing it into distinctive substructures are highly desired in clinic. However, traditional methods tend to be degraded by the large variances of heart and image, and also the high requirement in simultaneously distinguishing several substructures. In this paper, we start with the well-founded Fully Convolutional Network (FCN), and closely couple the FCN with 3D operators, transfer learning and deep supervision mechanism to distill 3D contextual information and attack potential difficulties in training deep neural networks. We then focus on a main concern in our enhanced FCN. As the number of substructures to be distinguished increases, the imbalance among different classes will emerge and bias the training towards major classes and therefore should be tackled seriously. Class-balanced loss function is useful in addressing the problem but at the risk of sacrificing the segmentation details. For a better trade-off, in this paper, we propose a hybrid loss which takes advantage of different kinds of loss functions to guide the training procedure to equally treat all classes, and at the same time preserve boundary details, like the branchy structure of great vessels. We verified our method on the MM-WHS Challenge 2017 datasets, which contain both CT and MR. Our hybrid loss guided model presents superior results in concurrently labeling 7 substructures of heart (ranked as second in CT segmentation Challenge). Our framework is robust and efficient on different modalities and can be extended to other volumetric segmentation tasks.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofStatistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 10663-
dc.titleHybrid loss guided convolutional networks for whole heart parsing-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-75541-0_23-
dc.identifier.scopuseid_2-s2.0-85044436506-
dc.identifier.spage215-
dc.identifier.epage223-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000550266300023-
dc.publisher.placeCham, Switzerland-

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