Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model
Abdominal Aortic Aneurysm (AAA) is still a socially relevant public health challenge, evidenced by an 82.1% increase in associated fatalities from 1990 to 2019 (with 172,427 deaths in 2019 alone). In a clinical setting, computed tomography angiography (CTA) is the imaging modality of choice for moni...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10850917/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859602184208384 |
|---|---|
| author | Merjulah Roby Juan C. Restrepo Haehwan Park Satish C. Muluk Mark K. Eskandari Seungik Baek Ender A. Finol |
| author_facet | Merjulah Roby Juan C. Restrepo Haehwan Park Satish C. Muluk Mark K. Eskandari Seungik Baek Ender A. Finol |
| author_sort | Merjulah Roby |
| collection | DOAJ |
| description | Abdominal Aortic Aneurysm (AAA) is still a socially relevant public health challenge, evidenced by an 82.1% increase in associated fatalities from 1990 to 2019 (with 172,427 deaths in 2019 alone). In a clinical setting, computed tomography angiography (CTA) is the imaging modality of choice for monitoring and/or presurgical planning of AAA patients. However, manual segmentation of CTA images is labor intensive and time consuming. Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. The deep-learning pipeline proposed in this work is designed to automatically segment AAA CTA images. The framework adapted a fully developed patch-based dilated modified U-Net model, which shows remarkable efficiency in accurately delineating AAA regions within the CTA scans. During the prediction phase, the deep learning architecture demonstrates exceptional speed, requiring <inline-formula> <tex-math notation="LaTeX">$17~\pm ~0.02$ </tex-math></inline-formula> milliseconds per frame to generate the final segmented output. Building upon this work, we included the application of Non-Uniform Rational B-Splines (NURBS) to enhance the segmentation process. This advancement is essential in addressing the critical need for clinical accuracy in medical image segmentation. NURBS enables the creation of continuous curves that seamlessly conform to the intricate contours of anatomical structures, offering a significant improvement in segmentation accuracy. Through the integration of advanced deep learning architectures and the precision of NURBS for segmentation refinement, coupled with the fast processing time and accurate segmentation, the the proposed model represents a promising clinical tool that can be used in the clinical management of AAAs. |
| format | Article |
| id | doaj-art-3bb1b11c116c4214ac3674b261dd027f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3bb1b11c116c4214ac3674b261dd027f2025-02-11T00:01:31ZengIEEEIEEE Access2169-35362025-01-0113245442455410.1109/ACCESS.2025.353341710850917Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet ModelMerjulah Roby0https://orcid.org/0009-0003-3786-4785Juan C. Restrepo1https://orcid.org/0009-0004-6032-7969Haehwan Park2https://orcid.org/0009-0003-2488-4808Satish C. Muluk3https://orcid.org/0000-0002-1025-961XMark K. Eskandari4Seungik Baek5https://orcid.org/0000-0003-2007-339XEnder A. Finol6https://orcid.org/0000-0002-0811-9482Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, USALG Electronics, Gasan Research and Development Campus, Seoul, South KoreaDepartment of Thoracic and Cardiovascular Surgery, Allegheny Health Network, Allegheny General Hospital, Pittsburgh, PA, USAFeinberg School of Medicine, Northwestern University, Chicago, IL, USADepartment of Mechanical Engineering, Michigan State University, East Lansing, MI, USADepartment of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, USAAbdominal Aortic Aneurysm (AAA) is still a socially relevant public health challenge, evidenced by an 82.1% increase in associated fatalities from 1990 to 2019 (with 172,427 deaths in 2019 alone). In a clinical setting, computed tomography angiography (CTA) is the imaging modality of choice for monitoring and/or presurgical planning of AAA patients. However, manual segmentation of CTA images is labor intensive and time consuming. Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. The deep-learning pipeline proposed in this work is designed to automatically segment AAA CTA images. The framework adapted a fully developed patch-based dilated modified U-Net model, which shows remarkable efficiency in accurately delineating AAA regions within the CTA scans. During the prediction phase, the deep learning architecture demonstrates exceptional speed, requiring <inline-formula> <tex-math notation="LaTeX">$17~\pm ~0.02$ </tex-math></inline-formula> milliseconds per frame to generate the final segmented output. Building upon this work, we included the application of Non-Uniform Rational B-Splines (NURBS) to enhance the segmentation process. This advancement is essential in addressing the critical need for clinical accuracy in medical image segmentation. NURBS enables the creation of continuous curves that seamlessly conform to the intricate contours of anatomical structures, offering a significant improvement in segmentation accuracy. Through the integration of advanced deep learning architectures and the precision of NURBS for segmentation refinement, coupled with the fast processing time and accurate segmentation, the the proposed model represents a promising clinical tool that can be used in the clinical management of AAAs.https://ieeexplore.ieee.org/document/10850917/Abdominal aortic aneurysmdeep learningimage segmentationcomputed tomography imagingNURBS |
| spellingShingle | Merjulah Roby Juan C. Restrepo Haehwan Park Satish C. Muluk Mark K. Eskandari Seungik Baek Ender A. Finol Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model IEEE Access Abdominal aortic aneurysm deep learning image segmentation computed tomography imaging NURBS |
| title | Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model |
| title_full | Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model |
| title_fullStr | Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model |
| title_full_unstemmed | Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model |
| title_short | Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model |
| title_sort | automatic segmentation of abdominal aortic aneurysm from computed tomography angiography using a patch based dilated unet model |
| topic | Abdominal aortic aneurysm deep learning image segmentation computed tomography imaging NURBS |
| url | https://ieeexplore.ieee.org/document/10850917/ |
| work_keys_str_mv | AT merjulahroby automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel AT juancrestrepo automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel AT haehwanpark automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel AT satishcmuluk automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel AT markkeskandari automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel AT seungikbaek automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel AT enderafinol automaticsegmentationofabdominalaorticaneurysmfromcomputedtomographyangiographyusingapatchbaseddilatedunetmodel |