Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder

by   Saddam Hussain Khan, et al.

Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder muscle MRI segmentation. The proposed RE-DAE harmoniously employs average and max-pooling operation in the encoder and decoder blocks of the Convolutional Neural Network (CNN). Region-based segmentation incorporated in the Deep Auto-Encoder (DAE) encourages the network to extract smooth and homogenous regions. In contrast, edge-based segmentation tries to learn the boundary and anatomical information. These two concepts, systematically combined in a DAE, generate a discriminative and sparse hybrid feature space (exploiting both region homogeneity and boundaries). Moreover, the concept of static attention is exploited in the proposed RE-DAE that helps in effectively learning the tear region. The performances of the proposed MRI segmentation based DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique. The MRI data has been collected from the Korea University Anam Hospital, Seoul, South Korea. Experimental comparisons have been conducted by employing innovative custom-made and existing pre-trained CNN architectures both using transfer learning and fine-tuning. Objective evaluation on the muscle datasets using the proposed SA-RE-DAE showed a dice similarity of 85.58 of 81.57 quality and the objective result suggest that the proposed SA-RE-DAE is able to correctly segment tear and muscle regions in shoulder muscle MRI for better clinical decisions.


page 4

page 7

page 11

page 12

page 15

page 16


Attentive Symmetric Autoencoder for Brain MRI Segmentation

Self-supervised learning methods based on image patch reconstruction hav...

Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks

In this paper, we propose a new deep learning framework for an automatic...

3D MRI brain tumor segmentation using autoencoder regularization

Automated segmentation of brain tumors from 3D magnetic resonance images...

Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks

COVID-19 is a global health problem. Consequently, early detection and a...

A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI

The segmentation of prostate whole gland and transition zone in Diffusio...

Segmentation of Defective Skulls from CT Data for Tissue Modelling

In this work we present a method of automatic segmentation of defective ...

Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

Accurate and robust segmentation of small organs in whole-body MRI is di...

Please sign up or login with your details

Forgot password? Click here to reset