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CT images with expert manual contours of thoracic cancer for benchmarking auto‐segmentation accuracy
Author(s) -
Yang Jinzhong,
Veeraraghavan Harini,
Elmpt Wouter,
Dekker Andre,
Gooding Mark,
Sharp Greg
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14107
Subject(s) - medicine , contouring , segmentation , radiation treatment planning , lung cancer , radiology , medical imaging , radiation therapy , medical physics , nuclear medicine , artificial intelligence , computer science , computer graphics (images)
Automatic segmentation offers many benefits for radiotherapy treatment planning; however, the lack of publicly available benchmark datasets limits the clinical use of automatic segmentation. In this work, we present a well-curated computed tomography (CT) dataset of high-quality manually drawn contours from patients with thoracic cancer that can be used to evaluate the accuracy of thoracic normal tissue auto-segmentation systems.
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