Frontiers in Oncology · 2022 · 12 citations · 27 references
MedicineOncologyRadiation OncologyBiomedical ImagingDiagnostic ImagingTreatment PlanningComputed TomographyMedical Image AnalysisMedical ImagingAuto-contouring For Target VolumesCervical Cancer RadiotherapyClinical Target Volume ContouringComputed Tomography Based ContouringContouringDiceRadiation Oncology ContouringSimilarity Coefficients
Purpose: Our purpose was to investigate the influence of atlas library size and CT cross-slice number on the accuracy and efficiency of the atlas-based auto-segmentation (ABAS) method for the automatic contouring of clinical treatment volume (CTV) and organs at risk (OARs) during cervical cancer radiotherapy. Methods: Of 140 cervical cancer patients, contours from 20, 40, 60, 80, 100, and 120 patients were selected incrementally to create six atlas library groups in ABAS. Another 20 tested patients were automatically contoured with the ABAS method and manually contoured by the same professional oncologist. Contours included CTV, bladder, rectum, femoral head-L, femoral head-R, and spinal cord. The CT cross-slice numbers of the 20 tested patients included 61, 65, 72, 75, 81, and 84. The index of dice similarity coefficients (DSCs) and Hausdorff distance (HD) were used to assess the consistency between ABAS automatic contouring and manual contouring. The randomized block analysis of variance and paired t-test were used for statistical analysis. Results: least significant difference comparison indicated that no significant difference had been found between different atlas library sizes with DSC and HD values. For ABAS efficiency, the atlas library size had no effect on the time of ABAS automatic contouring. The time of automatic contouring increased slightly with the increase in CT cross-slice numbers, which were 99.9, 106.8, 114.0, 120.6, 127.9, and 134.8 s with CT cross-slices of 61, 65, 72, 75, 81, and 84, respectively. Conclusion: A total of 20 atlas library sizes and a minimum CT cross-slice number including CTV and OARs are enough for ensuring the accuracy and efficiency of ABAS automatic contouring during cervical cancer radiotherapy.
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Karen Lim, William Small, Lorraine Portelance et al. · International Journal of Radiation Oncology*Biology*Physics · 2010 · 458 citations
Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma
Li Lin, Qi Dou, Yueming Jin et al. · Radiology · 2019 · 346 citations