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WFRF:(Ramel Jean Yves)
 

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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004847naa a2200745 4500
001oai:DiVA.org:kth-258723
003SwePub
008190910s2019 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2587232 URI
0247 a https://doi.org/10.1016/j.media.2019.1015372 DOI
040 a (SwePub)kth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Zhuang, Xiahai4 aut
2451 0a Evaluation of algorithms for Multi-Modality Whole Heart Segmentation :b An open-access grand challenge.
264 1b Elsevier BV,c 2019
338 a print2 rdacarrier
500 a QC 20190911. QC 20200109
520 a Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
650 7a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng
653 a Benchmark
653 a Challenge
653 a Multi-modality
653 a Whole Heart Segmentation
7001 a Li, Lei4 aut
7001 a Payer, Christian4 aut
7001 a Štern, Darko4 aut
7001 a Urschler, Martin4 aut
7001 a Heinrich, Mattias P4 aut
7001 a Oster, Julien4 aut
7001 a Wang, Chunliang,d 1980-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1tbkeej
7001 a Smedby, Örjan,d 1956-u KTH,Medicinsk avbildning,medicinsk bildbehandling och visualisering4 aut0 (Swepub:kth)u1vc2uzb
7001 a Bian, Cheng4 aut
7001 a Yang, Xin4 aut
7001 a Heng, Pheng-Ann4 aut
7001 a Mortazi, Aliasghar4 aut
7001 a Bagci, Ulas4 aut
7001 a Yang, Guanyu4 aut
7001 a Sun, Chenchen4 aut
7001 a Galisot, Gaetan4 aut
7001 a Ramel, Jean-Yves4 aut
7001 a Brouard, Thierry4 aut
7001 a Tong, Qianqian4 aut
7001 a Si, Weixin4 aut
7001 a Liao, Xiangyun4 aut
7001 a Zeng, Guodong4 aut
7001 a Shi, Zenglin4 aut
7001 a Zheng, Guoyan4 aut
7001 a Wang, Chengjia4 aut
7001 a MacGillivray, Tom4 aut
7001 a Newby, David4 aut
7001 a Rhode, Kawal4 aut
7001 a Ourselin, Sebastien4 aut
7001 a Mohiaddin, Raad4 aut
7001 a Keegan, Jennifer4 aut
7001 a Firmin, David4 aut
7001 a Yang, Guang4 aut
7102 a KTHb Medicinsk avbildning4 org
7730 t Medical Image Analysisd : Elsevier BVg 58q 58x 1361-8415x 1361-8423
8564 u https://doi.org/10.1016/j.media.2019.101537
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258723
8564 8u https://doi.org/10.1016/j.media.2019.101537

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