Unsupervised Image Segmentation on 2D Echocardiogram
Echocardiography is a widely used, non-invasive imaging technique for diagnosing and monitoring heart conditions. However, accurate segmentation of cardiac structures, particularly the left ventricle, remains a complex task due to the inherent variability and noise in echocardiographic images. Curre...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/515 |
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| author | Gabriel Farias Cacao Dongping Du Nandini Nair |
| author_facet | Gabriel Farias Cacao Dongping Du Nandini Nair |
| author_sort | Gabriel Farias Cacao |
| collection | DOAJ |
| description | Echocardiography is a widely used, non-invasive imaging technique for diagnosing and monitoring heart conditions. However, accurate segmentation of cardiac structures, particularly the left ventricle, remains a complex task due to the inherent variability and noise in echocardiographic images. Current supervised models have achieved state-of-the-art results but are highly dependent on large, annotated datasets, which are costly and time-consuming to obtain and depend on the quality of the annotated data. These limitations motivate the need for unsupervised methods that can generalize across different image conditions without relying on annotated data. In this study, we propose an unsupervised approach for segmenting 2D echocardiographic images. By combining customized objective functions with convolutional neural networks (CNNs), our method effectively segments cardiac structures, addressing the challenges posed by low-resolution and gray-scale images. Our approach leverages techniques traditionally used outside of medical imaging, optimizing feature extraction through CNNs in a data-driven manner and with a new and smaller network design. Another key contribution of this work is the introduction of a post-processing algorithm that refines the segmentation to isolate the left ventricle in both diastolic and systolic positions, enabling the calculation of the ejection fraction (EF). This calculation serves as a benchmark for evaluating the performance of our unsupervised method. Our results demonstrate the potential of unsupervised learning to improve echocardiogram analysis by overcoming the limitations of supervised approaches, particularly in settings where labeled data are scarce or unavailable. |
| format | Article |
| id | doaj-art-0368181588d44fa5bbf7db6d8349075d |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-0368181588d44fa5bbf7db6d8349075d2024-11-26T17:45:28ZengMDPI AGAlgorithms1999-48932024-11-01171151510.3390/a17110515Unsupervised Image Segmentation on 2D EchocardiogramGabriel Farias Cacao0Dongping Du1Nandini Nair2Department of Industrial, Manufacturing, and Systems Engineering (IMSE), Texas Tech University, Lubbock, TX 79409, USADepartment of Industrial, Manufacturing, and Systems Engineering (IMSE), Texas Tech University, Lubbock, TX 79409, USAPennState College of Medicine, Heart and Vascular Institute, Milton S. Hershey Medical Center, 500 University Dr, Hershey, PA 17033, USAEchocardiography is a widely used, non-invasive imaging technique for diagnosing and monitoring heart conditions. However, accurate segmentation of cardiac structures, particularly the left ventricle, remains a complex task due to the inherent variability and noise in echocardiographic images. Current supervised models have achieved state-of-the-art results but are highly dependent on large, annotated datasets, which are costly and time-consuming to obtain and depend on the quality of the annotated data. These limitations motivate the need for unsupervised methods that can generalize across different image conditions without relying on annotated data. In this study, we propose an unsupervised approach for segmenting 2D echocardiographic images. By combining customized objective functions with convolutional neural networks (CNNs), our method effectively segments cardiac structures, addressing the challenges posed by low-resolution and gray-scale images. Our approach leverages techniques traditionally used outside of medical imaging, optimizing feature extraction through CNNs in a data-driven manner and with a new and smaller network design. Another key contribution of this work is the introduction of a post-processing algorithm that refines the segmentation to isolate the left ventricle in both diastolic and systolic positions, enabling the calculation of the ejection fraction (EF). This calculation serves as a benchmark for evaluating the performance of our unsupervised method. Our results demonstrate the potential of unsupervised learning to improve echocardiogram analysis by overcoming the limitations of supervised approaches, particularly in settings where labeled data are scarce or unavailable.https://www.mdpi.com/1999-4893/17/11/515unsupervised learningimage segmentationechocardiographyconvolutional neural networksmedical imaging |
| spellingShingle | Gabriel Farias Cacao Dongping Du Nandini Nair Unsupervised Image Segmentation on 2D Echocardiogram Algorithms unsupervised learning image segmentation echocardiography convolutional neural networks medical imaging |
| title | Unsupervised Image Segmentation on 2D Echocardiogram |
| title_full | Unsupervised Image Segmentation on 2D Echocardiogram |
| title_fullStr | Unsupervised Image Segmentation on 2D Echocardiogram |
| title_full_unstemmed | Unsupervised Image Segmentation on 2D Echocardiogram |
| title_short | Unsupervised Image Segmentation on 2D Echocardiogram |
| title_sort | unsupervised image segmentation on 2d echocardiogram |
| topic | unsupervised learning image segmentation echocardiography convolutional neural networks medical imaging |
| url | https://www.mdpi.com/1999-4893/17/11/515 |
| work_keys_str_mv | AT gabrielfariascacao unsupervisedimagesegmentationon2dechocardiogram AT dongpingdu unsupervisedimagesegmentationon2dechocardiogram AT nandininair unsupervisedimagesegmentationon2dechocardiogram |