Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images

Autor:	Ahmad, Ezak, Goyal, Manu, McPhee, Jamie S., Degens, Hans, Yap, Moi Hoon
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This paper presents an end-to-end solution for MRI thigh quadriceps segmentation. This is the first attempt that deep learning methods are used for the MRI thigh segmentation task. We use the state-of-the-art Fully Convolutional Networks with transfer learning approach for the semantic segmentation of regions of interest in MRI thigh scans. To further improve the performance of the segmentation, we propose a post-processing technique using basic image processing methods. With our proposed method, we have established a new benchmark for MRI thigh quadriceps segmentation with mean Jaccard Similarity Index of 0.9502 and processing time of 0.117 second per image. Comment: 27 pages, 7 figures and 5 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1801.00415 Zobrazit plný text záznamu View this record from Arxiv