作者
Clément Zotti, Zhiming Luo, Olivier Humbert, Alain Lalande, Pierre-Marc Jodoin
发表日期
2018
研讨会论文
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 8
页码范围
73-81
出版商
Springer International Publishing
简介
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv “grid” architecture which can be seen as an extension of the U-Net.
Experimental results reveal that our method can segment the left …
学术搜索中的文章
C Zotti, Z Luo, O Humbert, A Lalande, PM Jodoin - Statistical Atlases and Computational Models of the …, 2018
C Zotti, Z Luo, A Lalande, O Humbert, PM Jodoin - arXiv preprint arXiv:1705.08943, 2017