Kostandy, Petro

PurposeOrgan segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs‐at‐risk, which is laborious and time‐consuming. We present a fully automated segmentation method based on the three‐dimensional (3D) U‐Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels. Whole abdomen segmentation is capable of quantifying exposure beyond a handful of organs‐at‐risk to all tissues within the abdomen.MethodsSixty‐six (66) CT examinations of 64 individuals were included in the training and validation sets and 18 CT examinations from 16 individuals were included in the test set. All pixels in each examination were segmented by image analysts (with physician correction) and assigned one of 33 labels. Segmentation was performed with a 3D U‐Net variant architecture which included residual blocks, and model performance was quantified on 18 test cases. Human interobserver variability (using semiautomated segmentation) was also reported on two scans, and manual interobserver variability of three individuals was reported on one scan. Model performance was also compared to several of the best models reported in the literature for multiple organ segmentation.ResultsThe accuracy of the 3D U‐Net model ranges from a Dice coefficient of 0.95 in the liver, 0.93 in the kidneys, 0.79 in the pancreas, 0.69 in the adrenals, and 0.51 in the renal arteries. Model accuracy is within 5% of human segmentation in eight of 19 organs and within 10% accuracy in 13 of 19 organs.ConclusionsThe CNN approaches the accuracy of human tracers and on certain complex organs displays more consistent prediction than human tracers. Fully automated deep learning‐based segmentation of CT abdomen has the potential to improve both the speed and accuracy of radiotherapy dose prediction for organs‐at‐risk.

Importance Preoperative assessment of nasal soft-tissue envelope (STE) thickness is an important component of rhinoplasty that presently lacks validated tools. Objective To measure and assess the distribution of nasal STE thickness in a large patient population and to determine if facial plastic surgery clinicians can predict nasal STE thickness based on visual examination of the nose. Design, Setting, and Participants This retrospective review and prospective assessment of 190 adult patients by 4 expert raters was conducted at an academic tertiary referral center. The patients had high-resolution maxillofacial computed tomography (CT) scans and standardized facial photographs on file and did not have a history of nasal fracture, septal perforation, rhinoplasty, or other surgery or medical conditions altering nasal form. Data were analyzed in March 2019. Main Outcomes and Measures Measure nasal STE thickness at defined anatomic subsites using high-resolution CT scans. Measure expert-predicted nasal STE thickness based on visual examination of the nose using a scale from 0 (thinnest) to 100 (thickest). Results Of the 190 patients, 78 were women and the mean (SD) age was 45 (17) years. The nasal STE was thickest at the sellion (mean [SD]) (6.7 [1.7] mm), thinnest at the rhinion (2.1 [0.7] mm), thickened over the supratip (4.8 [1.0] mm) and nasal tip (3.1 [0.6] mm), and thinned over the columella (2.6 [0.4] mm). In the study population, nasal STE thickness followed a nearly normal distribution for each measured subsite, with the majority of patients in a medium thickness range. Comparison of predicted and actual nasal STE thickness showed that experts could accurately predict nasal STE thickness, with the highest accuracy at the nasal tip ( r , 0.73; prediction accuracy, 91%). A strong positive correlation was noted among the experts’ STE estimates ( r , 0.83-0.89), suggesting a high level of agreement between individual raters. Conclusions and Relevance There is variable thickness of the nasal STE, which influences the external nasal contour and rhinoplasty outcomes. With visual analysis of the nose, experts can agree on and predict nasal STE thickness, with the highest accuracy at the nasal tip. These data can aid in preoperative planning for rhinoplasty, allowing implementation of preoperative, intraoperative, and postoperative strategies to optimize the nasal STE, which may ultimately improve patient outcomes and satisfaction. Level of Evidence NA .