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.
In 1992, @cite_41 made an attempt on MRI thigh segmentation by using neural network. The network model integrates a probabilistic neural network to facilitate the generation of probability estimates at each pixel for use in an iterative segmentation process. This preliminary study on MRI thigh segmentation by using one of the simplest forms of neural network reported that the segmentation result is unaffected by the quality of the training dataset. Today, the concept of neural network has been revolutionised and diversely applied, including in segmentation of medical imaging.
@cite_41
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A network structure for segmenting magnetic resonance medical images is proposed. The incorporation of a probabilistic neural network structure into the segmentation process allows decisions regarding the characterization of each pixel to be made in a probabilistic manner, thus reducing the effect of an incorrect decision early in the process on the final segmentation result. The probabilistic neural network facilitates the generation of likelihood estimates for use in an iterative segmentation process, which was shown to produce good segmentation results on real magnetic resonance images. >