Cargando…
A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study
Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417427/ https://www.ncbi.nlm.nih.gov/pubmed/37568868 http://dx.doi.org/10.3390/diagnostics13152504 |
_version_ | 1785088032757514240 |
---|---|
author | Wu, Shu Yu, Hang Li, Cuiping Zheng, Rencheng Xia, Xueqin Wang, Chengyan Wang, He |
author_facet | Wu, Shu Yu, Hang Li, Cuiping Zheng, Rencheng Xia, Xueqin Wang, Chengyan Wang, He |
author_sort | Wu, Shu |
collection | PubMed |
description | Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%. |
format | Online Article Text |
id | pubmed-10417427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104174272023-08-12 A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study Wu, Shu Yu, Hang Li, Cuiping Zheng, Rencheng Xia, Xueqin Wang, Chengyan Wang, He Diagnostics (Basel) Article Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%. MDPI 2023-07-27 /pmc/articles/PMC10417427/ /pubmed/37568868 http://dx.doi.org/10.3390/diagnostics13152504 Text en © 2023 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 Wu, Shu Yu, Hang Li, Cuiping Zheng, Rencheng Xia, Xueqin Wang, Chengyan Wang, He A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study |
title | A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study |
title_full | A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study |
title_fullStr | A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study |
title_full_unstemmed | A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study |
title_short | A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study |
title_sort | coarse-to-fine fusion network for small liver tumor detection and segmentation: a real-world study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417427/ https://www.ncbi.nlm.nih.gov/pubmed/37568868 http://dx.doi.org/10.3390/diagnostics13152504 |
work_keys_str_mv | AT wushu acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT yuhang acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT licuiping acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT zhengrencheng acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT xiaxueqin acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT wangchengyan acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT wanghe acoarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT wushu coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT yuhang coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT licuiping coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT zhengrencheng coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT xiaxueqin coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT wangchengyan coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy AT wanghe coarsetofinefusionnetworkforsmalllivertumordetectionandsegmentationarealworldstudy |