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Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network

Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network

Authors :
Saravanan Srinivasan
Kirubha Durairaju
K. Deeba
Sandeep Kumar Mathivanan
P. Karthikeyan
Mohd Asif Shah
Source :
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-20 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.

Details

Language :
English
ISSN :
14712342
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Medical Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.426e89aee9464ca6b550e850b097c29d
Document Type :
article
Full Text :
https://doi.org/10.1186/s12880-024-01197-5