Deep Learning Architectures and Techniques for Multi-organ Segmentation

Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results and achievements outweighed the older techniques. Due to improvements in the computer hardware and the development of specialized network desi...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 12; no. 1
Main Authors: Ogrean, Valentin, Dorobantiu, Alexandru, Brad, Remus
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2021
Subjects:
Artificial neural networks
Deep learning
Generative adversarial networks
Neural networks
Recurrent neural networks
Segmentation
ISSN:2158-107X, 2156-5570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results and achievements outweighed the older techniques. Due to improvements in the computer hardware and the development of specialized network designs, deep learning segmentation presents exciting developments and opportunities also for future research. Therefore, we have compiled a review of the most interesting deep learning architectures applicable to medical multi-organ segmentation. We have summarized over 50 contributions, most of which are more recent than 3 years. The papers were grouped into three categories based on the architecture: “Convolutional Neural Networks” (CNNs), “Fully Convolutional Neural Networks” (FCNs) and hybrid architectures that combine more designs - including “Generative Adversarial Networks” (GANs) or “Recurrent Neural Networks” (RNNs). Afterwards we present the most used multi-organ datasets, and we finalize by making a general discussion of current shortcomings and future potential research paths.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120104