التفاصيل البيبلوغرافية
| العنوان: |
Interactive contouring through contextual deep learning |
| المؤلفون: |
Trimpl, Michael J., Boukerroui, Djamal, Stride, Eleanor P. J., Vallis, Katherine A., Gooding, Mark J. |
| المصدر: |
Medical Physics ; volume 48, issue 6, page 2951-2959 ; ISSN 0094-2405 2473-4209 |
| بيانات النشر: |
Wiley |
| سنة النشر: |
2021 |
| المجموعة: |
Wiley Online Library (Open Access Articles via Crossref) |
| الوصف: |
Purpose To investigate a deep learning approach that enables three‐dimensional (3D) segmentation of an arbitrary structure of interest given a user provided two‐dimensional (2D) contour for context. Such an approach could decrease delineation times and improve contouring consistency, particularly for anatomical structures for which no automatic segmentation tools exist. Methods A series of deep learning segmentation models using a Recurrent Residual U‐Net with attention gates was trained with a successively expanding training set. Contextual information was provided to the models, using a previously contoured slice as an input, in addition to the slice to be contoured. In total, 6 models were developed, and 19 different anatomical structures were used for training and testing. Each of the models was evaluated for all 19 structures, even if they were excluded from the training set, in order to assess the model's ability to segment unseen structures of interest. Each model's performance was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance, and relative added path length (APL). Results The segmentation performance for seen and unseen structures improved when the training set was expanded by addition of structures previously excluded from the training set. A model trained exclusively on heart structures achieved a DSC of 0.33, HD of 44 mm, and relative APL of 0.85 when segmenting the spleen, whereas a model trained on a diverse set of structures, but still excluding the spleen, achieved a DSC of 0.80, HD of 13 mm, and relative APL of 0.35. Iterative prediction performed better compared to direct prediction when considering unseen structures. Conclusions Training a contextual deep learning model on a diverse set of structures increases the segmentation performance for the structures in the training set, but importantly enables the model to generalize and make predictions even for unseen structures that were not represented in the training set. This shows that user‐provided context can be ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1002/mp.14852 |
| الإتاحة: |
https://doi.org/10.1002/mp.14852Test |
| حقوق: |
http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.771DF4E2 |
| قاعدة البيانات: |
BASE |
