Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images
It addresses a domain-specific medical imaging task for MRI thigh segmentation, establishing a new benchmark but is incremental as it applies existing methods to a new dataset.
This paper tackles the problem of segmenting human thigh quadriceps muscle in MRI images using deep learning, achieving a mean Jaccard Similarity Index of 0.9502 and a processing time of 0.117 seconds per image.
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.