Cargando…
Exploiting Global Structure Information to Improve Medical Image Segmentation
In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method i...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125827/ https://www.ncbi.nlm.nih.gov/pubmed/34067205 http://dx.doi.org/10.3390/s21093249 |
_version_ | 1783693615945482240 |
---|---|
author | Hwang, Jaemoon Hwang, Sangheum |
author_facet | Hwang, Jaemoon Hwang, Sangheum |
author_sort | Hwang, Jaemoon |
collection | PubMed |
description | In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts. |
format | Online Article Text |
id | pubmed-8125827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81258272021-05-17 Exploiting Global Structure Information to Improve Medical Image Segmentation Hwang, Jaemoon Hwang, Sangheum Sensors (Basel) Article In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts. MDPI 2021-05-07 /pmc/articles/PMC8125827/ /pubmed/34067205 http://dx.doi.org/10.3390/s21093249 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hwang, Jaemoon Hwang, Sangheum Exploiting Global Structure Information to Improve Medical Image Segmentation |
title | Exploiting Global Structure Information to Improve Medical Image Segmentation |
title_full | Exploiting Global Structure Information to Improve Medical Image Segmentation |
title_fullStr | Exploiting Global Structure Information to Improve Medical Image Segmentation |
title_full_unstemmed | Exploiting Global Structure Information to Improve Medical Image Segmentation |
title_short | Exploiting Global Structure Information to Improve Medical Image Segmentation |
title_sort | exploiting global structure information to improve medical image segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125827/ https://www.ncbi.nlm.nih.gov/pubmed/34067205 http://dx.doi.org/10.3390/s21093249 |
work_keys_str_mv | AT hwangjaemoon exploitingglobalstructureinformationtoimprovemedicalimagesegmentation AT hwangsangheum exploitingglobalstructureinformationtoimprovemedicalimagesegmentation |