Academic Journal
SmartSeg: machine learning infrastructure software for accelerating medical image segmentation in patient-specific applications
| العنوان: | SmartSeg: machine learning infrastructure software for accelerating medical image segmentation in patient-specific applications |
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| المؤلفون: | William Burton, Kalin Gibbons, Keaghan Economon |
| المصدر: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol 12, Iss 1 (2024) |
| بيانات النشر: | Taylor & Francis Group, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Biotechnology |
| مصطلحات موضوعية: | Machine learning, automatic segmentation, medical images, Biotechnology, TP248.13-248.65 |
| الوصف: | Patient-specific applications in biomechanics and orthopaedics call for segmentation of volumetric medical images. This task has historically been performed manually or semi-automatically, which entails significant effort by trained experts. Computer vision algorithms based on machine learning have emerged as an effective tool for accelerating medical image processing tasks through automation. The current work describes fully no-code software which supports development of custom machine learning models for automatic segmentation of volumetric medical images. This cloud-integrated desktop application allows researchers and biomedical engineers to define segmentation algorithms, configure annotated datasets, and train personalised machine learning algorithms with cloud compute in just a few clicks. A presented case study demonstrates an ideal workflow in the software using femoral cartilage segmentation from magnetic resonance imaging as a representative use case. Models developed in the software demonstrated mean dice similarity coefficients of up to 0.886 on a test cohort, which is competitive with previously reported methods developed with significantly larger data sets. The mean surface difference between digitised models reconstructed from ground truth and predicted segmentation was 0.33 mm. Results suggest the described software enables creation of accurate machine learning models with limited engineering effort. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 21681163 2168-1171 2168-1163 |
| Relation: | https://doaj.org/toc/2168-1163; https://doaj.org/toc/2168-1171 |
| DOI: | 10.1080/21681163.2024.2415716 |
| URL الوصول: | https://doaj.org/article/427a1622b47b433f98a1dee9d396712e |
| رقم الانضمام: | edsdoj.427a1622b47b433f98a1dee9d396712e |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 21681163 21681171 |
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| DOI: | 10.1080/21681163.2024.2415716 |