Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume
| Autor: | David Edmunds, Jonas Söderberg, Thomas Bortfeld, Fredrik Löfman, Helen A. Shih, Nadya Shusharina |
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| Rok vydání: | 2019 |
| Předmět: |
Similarity (geometry)
Computer science Tentorium cerebelli Planning target volume computer.software_genre Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Voxel Humans Radiology Nuclear Medicine and imaging Radiation treatment planning business.industry Radiotherapy Planning Computer-Assisted Pattern recognition Hematology Glioma Tumor Burden Falx cerebri Oncology 030220 oncology & carcinogenesis Artificial intelligence Neural Networks Computer business Dijkstra's algorithm computer Algorithms |
| Zdroj: | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 146 |
| ISSN: | 1879-0887 |
| Popis: | Purpose Delineation of the clinical target volume (CTV) is arguably the weakest link in the treatment planning chain. This work aims to support clinicians in this crucial task. Methods and materials While the CTV itself is ambiguous, it is much easier to identify structures that do not belong to the CTV and serve as barriers to the spread of the disease. We segment the known barrier structures using a convolutional neural network (CNN). The CTV is then obtained by starting from the manually delineated gross tumor volume (GTV) and expanding it while taking into account the barrier structures. Mathematically, we define the CTV as an iso-surface in the 3D map of shortest paths of all voxels from the GTV. The shortest paths are found with the Dijkstra algorithm. While the method is generally applicable, we test it on 206 glioma and glioblastoma cases. Results The auto-segmented barrier structures for the brain cases include the ventricles, falx cerebri, tentorium cerebelli, brain sinuses, and the outer surface of the brain. Manual and auto-segmented barrier structures agree with surface Dice Similarity Coefficients (DSC) ranging from 0.91 to 0.97 at 2 mm tolerance. Comparison of manual and automatically delineated CTVs shows a median surface DSC of 0.79. Conclusions Barrier structures for CTV definition can be auto-delineated with outstanding precision using a CNN. An algorithm for automated calculation of the CTV by 3D expansion of the GTV while respecting anatomical barriers has been developed. It shows good agreement with manual CTV definition for brain tumors. |
| Databáze: | OpenAIRE |
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