Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning
<b>Background/Objective:</b> Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical assessments rely heavily on manual review of medical imaging, a process that is time-consuming and prone to i...
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MDPI AG
2025-07-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/14/1804 |
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| author | Alireza Bagheri Rajeoni Breanna Pederson Susan M. Lessner Homayoun Valafar |
| author_facet | Alireza Bagheri Rajeoni Breanna Pederson Susan M. Lessner Homayoun Valafar |
| author_sort | Alireza Bagheri Rajeoni |
| collection | DOAJ |
| description | <b>Background/Objective:</b> Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical assessments rely heavily on manual review of medical imaging, a process that is time-consuming and prone to inter-observer variability. The widely accepted standard of care primarily focuses on measuring aneurysm diameter at its widest point, providing a limited perspective on aneurysm morphology and lacking efficient methods to measure aneurysm volumes. Yet, volume measurement can offer deeper insight into aneurysm progression and severity. In this study, we propose an automated approach that leverages the strengths of pre-trained neural networks and expert systems to delineate aneurysm boundaries and compute volumes on an unannotated dataset from 60 patients. The dataset includes slice-level start/end annotations for aneurysm but no pixel-wise aorta segmentations. <b>Method:</b> Our method utilizes a pre-trained UNet to automatically locate the aorta, employs SAM2 to track the aorta through vascular irregularities such as aneurysms down to the iliac bifurcation, and finally uses a Long Short-Term Memory (LSTM) network or expert system to identify the beginning and end points of the aneurysm within the aorta. <b>Results:</b> Despite no manual aorta segmentation, our approach achieves promising accuracy, predicting the aneurysm start point with an <i>R</i><sup>2</sup> score of 71%, the end point with an <i>R</i><sup>2</sup> score of 76%, and the volume with an <i>R</i><sup>2</sup> score of 92%. <b>Conclusions:</b> This technique has the potential to facilitate large-scale aneurysm analysis and improve clinical decision-making by reducing dependence on annotated datasets. |
| format | Article |
| id | doaj-art-0681bbb3a89e42628a3e33fdaa773a4f |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-0681bbb3a89e42628a3e33fdaa773a4f2025-08-20T03:32:26ZengMDPI AGDiagnostics2075-44182025-07-011514180410.3390/diagnostics15141804Automated Aneurysm Boundary Detection and Volume Estimation Using Deep LearningAlireza Bagheri Rajeoni0Breanna Pederson1Susan M. Lessner2Homayoun Valafar3Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USADepartment Health, Sport, and Human Physiology, University of Iowa, Iowa City, IA 52242, USADepartment of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC 29209, USADepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA<b>Background/Objective:</b> Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical assessments rely heavily on manual review of medical imaging, a process that is time-consuming and prone to inter-observer variability. The widely accepted standard of care primarily focuses on measuring aneurysm diameter at its widest point, providing a limited perspective on aneurysm morphology and lacking efficient methods to measure aneurysm volumes. Yet, volume measurement can offer deeper insight into aneurysm progression and severity. In this study, we propose an automated approach that leverages the strengths of pre-trained neural networks and expert systems to delineate aneurysm boundaries and compute volumes on an unannotated dataset from 60 patients. The dataset includes slice-level start/end annotations for aneurysm but no pixel-wise aorta segmentations. <b>Method:</b> Our method utilizes a pre-trained UNet to automatically locate the aorta, employs SAM2 to track the aorta through vascular irregularities such as aneurysms down to the iliac bifurcation, and finally uses a Long Short-Term Memory (LSTM) network or expert system to identify the beginning and end points of the aneurysm within the aorta. <b>Results:</b> Despite no manual aorta segmentation, our approach achieves promising accuracy, predicting the aneurysm start point with an <i>R</i><sup>2</sup> score of 71%, the end point with an <i>R</i><sup>2</sup> score of 76%, and the volume with an <i>R</i><sup>2</sup> score of 92%. <b>Conclusions:</b> This technique has the potential to facilitate large-scale aneurysm analysis and improve clinical decision-making by reducing dependence on annotated datasets.https://www.mdpi.com/2075-4418/15/14/1804vasculature segmentationdeep learningimage segmentationaneurysmaneurysm boundary detectionaneurysm volume measurement |
| spellingShingle | Alireza Bagheri Rajeoni Breanna Pederson Susan M. Lessner Homayoun Valafar Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning Diagnostics vasculature segmentation deep learning image segmentation aneurysm aneurysm boundary detection aneurysm volume measurement |
| title | Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning |
| title_full | Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning |
| title_fullStr | Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning |
| title_full_unstemmed | Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning |
| title_short | Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning |
| title_sort | automated aneurysm boundary detection and volume estimation using deep learning |
| topic | vasculature segmentation deep learning image segmentation aneurysm aneurysm boundary detection aneurysm volume measurement |
| url | https://www.mdpi.com/2075-4418/15/14/1804 |
| work_keys_str_mv | AT alirezabagherirajeoni automatedaneurysmboundarydetectionandvolumeestimationusingdeeplearning AT breannapederson automatedaneurysmboundarydetectionandvolumeestimationusingdeeplearning AT susanmlessner automatedaneurysmboundarydetectionandvolumeestimationusingdeeplearning AT homayounvalafar automatedaneurysmboundarydetectionandvolumeestimationusingdeeplearning |