TY - GEN
T1 - 3D deeply-supervised U-net based whole heart segmentation
AU - Tong, Qianqian
AU - Ning, Munan
AU - Si, Weixin
AU - Liao, Xiangyun
AU - Qin, Jing
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Accurate whole-heart segmentation from multi-modality medical images (MRI, CT) plays an important role in many clinical applications, such as precision surgical planning and improvement of diagnosis and treatment. This paper presents a deeply-supervised 3D U-Net for fully automatic whole-heart segmentation by jointly using the multi-modal MRI and CT images. First, a 3D U-Net is employed to coarsely detect the whole heart and segment its region of interest, which can alleviate the impact of surrounding tissues. Then, we artificially enlarge the training set by extracting different regions of interest so as to train a deep network. We perform voxel-wise whole-heart segmentation with the end-to-end trained deeply-supervised 3D U-Net. Considering that different modality information of the whole heart has a certain complementary effect, we extract multi-modality features by fusing MRI and CT images to define the overall heart structure, and achieve final results. We evaluate our method on cardiac images from the multi-modality whole heart segmentation (MM-WHS) 2017 challenge.
AB - Accurate whole-heart segmentation from multi-modality medical images (MRI, CT) plays an important role in many clinical applications, such as precision surgical planning and improvement of diagnosis and treatment. This paper presents a deeply-supervised 3D U-Net for fully automatic whole-heart segmentation by jointly using the multi-modal MRI and CT images. First, a 3D U-Net is employed to coarsely detect the whole heart and segment its region of interest, which can alleviate the impact of surrounding tissues. Then, we artificially enlarge the training set by extracting different regions of interest so as to train a deep network. We perform voxel-wise whole-heart segmentation with the end-to-end trained deeply-supervised 3D U-Net. Considering that different modality information of the whole heart has a certain complementary effect, we extract multi-modality features by fusing MRI and CT images to define the overall heart structure, and achieve final results. We evaluate our method on cardiac images from the multi-modality whole heart segmentation (MM-WHS) 2017 challenge.
KW - 3D deeply-supervised U-Net
KW - Multi-modal cardiac images
KW - Whole heart segmentation
UR - http://www.scopus.com/inward/record.url?scp=85044469459&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75541-0_24
DO - 10.1007/978-3-319-75541-0_24
M3 - Conference article published in proceeding or book
SN - 9783319755403
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 224
EP - 232
BT - Statistical Atlases and Computational Models of the Heart
A2 - Bernard, Olivier
A2 - Jodoin, Pierre-Marc
A2 - Zhuang, Xiahai
A2 - Yang, Guang
A2 - Young, Alistair
A2 - Sermesant, Maxime
A2 - Lalande, Alain
A2 - Pop, Mihaela
PB - Springer Verlag
T2 - 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017
Y2 - 10 September 2017 through 14 September 2017
ER -