Autosegmentation of the rectum on megavoltage image guidance scans

Abstract

Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

Use of deep learning to segment bolus during videofluoroscopic swallow studies

“…In other words, we may discover that truly accurate understanding of this mechanism may be beyond the capabilities of perceptual assessment of instrumental findings thereby requiring further technological supplementation. Furthermore, just as other fields of healthcare have already benefited from the implementation of AI, [5,[14][15][16] it can be expected that the field of dysphagia stands to benefit as well. The authors would like to emphasize that this implementation should be viewed as an opportunity for advancement of the field for clinicians, as opposed to a loss of clinical responsibility or autonomy.…”

Feasibility of artificial intelligence-driven interfractional monitoring of organ changes by mega-voltage computed tomography in intensity-modulated radiotherapy of prostate cancer

“…In a previous study by Shelley et al [ 43 ], an auto-segmentation model for the rectum in MVCT images of prostate cancer patients was developed. This model was based on the Chan-Vese algorithm and implemented in MATLAB.…”

Associations between voxel-level accumulated dose and rectal toxicity in prostate radiotherapy

“…Kilovoltage (kV) planning scans were manually contoured, and the rectum was defined from the rectosigmoid junction to the most inferior slice containing both ischial tuberosities [31]. For daily MVCT scans, the rectum was identified using a locally developed autosegmentation system based on the Chan-Vese algorithm [32]. Manual contouring of the 4174 MVCT scans (over 62,000 slices) in this study would not have been practicable, and the autosegmentation system has previously been shown to fall within an acceptable range for intra-and inter-observer variability [33].…”