CTContour : an open-source Python pipeline for automatic contouring and calculation of mean SSDE along the abdomino-pelvic region for CT images ; validation on fifteen systems

Abstract

Purpose: Calculation of the Size Specific Dose Estimate (SSDE) requires accurate delineation of the skin boundary of patient CT slices. The AAPM recommendation for SSDE evaluation at every CT slice is too time intensive for manual contouring, prohibiting real-time or bulk processing; an automated approach is therefore desirable. Previous automated delineation studies either did not fully disclose the steps of the algorithm or did not always manage to fully isolate the patient. The purpose of this study was to develop a validated, freely available, fast, vendor-independent open-source tool to automatically and accurately contour and calculate the SSDE for the abdomino-pelvic region for entire studies in real-time, including flagging of patient-truncated images. Methods: The Python tool, CTContour, consists of a sequence of morphological steps and scales over multiple cores for speed. Tool validation was achieved on 700 randomly selected slices from abdominal and abdomino-pelvic studies from public datasets. Contouring accuracy was assessed visually by four medical physicists using a 1–5 Likert scale (5 indicating perfect contouring). Mean SSDE values were validated via manual calculation. Results: Contour accuracy validation produced a score of four of five for 98.5 % of the images. A 300 slice exam was contoured and truncation flagged in 6.3 s on a six-core laptop. Conclusions: The algorithm was accurate even for complex clinical scenarios and when artefacts were present. Fast execution makes it possible to automate the calculation of SSDE in real time. The tool has been published on GitHub under the GNU-GPLv3 license.