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

Source: [Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images](<https://www.sota2.com/research/paper/68fa64bb1e209131cea061d6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:09:58.940Z

Authors: Ezak Ahmad, Manu Goyal, Jamie S. McPhee, Hans Degens, Moi Hoon Yap

Published: 2018-01-01T08:58:10.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1801.00415>)

[PDF](<https://arxiv.org/pdf/1801.00415.pdf>)