# Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images

arXiv: https://arxiv.org/abs/1801.00415 · HTML: https://mlsanity.com/paper/1801.00415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Automated Kidney Segmentation by Mask R-CNN in T2-weighted Magnetic Resonance Imaging](https://mlsanity.com/paper/2108.12506/) (2021)

## Abstract (arXiv)

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.