###### Abstract  
For prostate cancer patients, large organ deformations occurring between radiotherapy treatment sessions create uncertainty about the doses delivered to the tumor and surrounding healthy organs. Segmenting those regions on cone beam CT (CBCT) scans acquired on treatment day would reduce such uncertainties. In this work, a 3D U-net deep-learning architecture was trained to segment bladder, rectum, and prostate on CBCT scans. Due to the scarcity of contoured CBCT scans, the training set was augmented with CT scans already contoured in the current clinical workflow. Our network was then tested on 63 CBCT scans. The Dice similarity coefficient (DSC) increased significantly with the number of CBCT and CT scans in the training set, reaching 0.874 \(\pm\) 0.096, 0.814 \(\pm\) 0.055, and 0.758 \(\pm\) 0.101 for bladder, rectum, and prostate, respectively. This was about 10% better than conventional approaches based on deformable image registration between planning CT and treatment CBCT scans, except for prostate. Interestingly, adding 74 CT scans to the CBCT training set allowed maintaining high DSCs, while halving the number of CBCT scans. Hence, our work showed that although CBCT scans included artifacts, cross-domain augmentation of the training set was effective and could rely on large datasets available for planning CT scans. segmentation; deep-learning; deformable image registration; cone beam CT; pelvis; prostate cancer; radiotherapy; CNN; U-net +
Footnote †: journal: Medical  
currently used in order to detect daily variations in patient anatomy, which are particularly large in the pelvic region due to physiological function (e.g., bladder and rectal filling and voiding). Detecting such variations is important since they can impair treatment dose conformity, which means delivering too large a dose to the healthy organs (e.g., the bladder and rectum in the case of prostate cancer) and too low a dose to the clinical target volume (which simply corresponds to prostate itself for a significant proportion of patients) [2]. To improve treatment dose conformity in the pelvic region further, proposals have been made to change treatment plan delivery as a function of time based on observed anatomic variations [3,4]. However, a step towards better adaptive radiotherapy would require automatic segmentation of the pelvic organs on daily CBCT scans in order to measure the anatomical variations accurately. Automating this segmentation is necessary to be able to integrate it in the clinical workflow, as delineating the organs manually on daily scans is excessively time consuming. Measuring anatomical variations is particularly important in proton therapy because the proton dose distribution is highly sensitive to changes in patient geometry [5,6]. Currently, organ segmentation is classically performed by deformable image registration (DIR) algorithms between the planning CT and daily CBCT scans [7,8]. These algorithms include such clinical software packages as MIM [9] and RayStation [10]. Although the results are better than those of rigid registration, these intensity-based DIR algorithms fail in the presence of large deformations between the registered scans, as is the case in the pelvic region [11,12]. [PERSON] et al. [11] and [PERSON] et al. [12] implemented a featurelet-based algorithm [13] and the demons DIR algorithm [14], respectively. As a result, more complex DIR approaches, such as a B-spline DIR algorithm relying on mutual information, have been proposed [15]. This last approach implements a six-pass DIR with progressively finer resolution and, after visual inspection, an optional final pass using a narrow region around the region of interest. Another approach uses a DIR framework where a locally rigid deformation is enforced for bone and/or prostate, while surrounding tissue is still allowed to deform elastically [16]. Alternatively, statistical shape models can capture shape variations and have also been considered for bladder segmentation on CBCT scans [17,18].