Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT

Abstract

a b s t r a c tAim: The segmentation of organs from a CT scan is a time-consuming task, which is one hindrance for adaptive radiation therapy. Through deep learning, it is possible to automatically delineate organs. Metrics like dice score do not necessarily represent the impact for clinical practice. Therefore, a clinical evaluation of the deep neural network is needed to verify the segmentation quality. Methods: In this work, a novel deep neural network is trained on 300 CT and 300 artificially generated pseudo CBCTs to segment bladder, prostate, rectum and seminal vesicles from CT and cone beam CT scans. The model is evaluated on 45 CBCT and 5 CT scans through a clinical review performed by three different clinics located in Europe, North America and Australia. Results: The deep learning model is scored either equally good (prostate and seminal vesicles) or better (bladder and rectum) than the structures from routine clinical practice. No or minor corrections are required for 97.5% of the segmentations of the bladder, 91.5% of the prostate, 94% of the rectum and seminal vesicles. Overall, for 82.5% of the patients none of the organs need major corrections or a redraw. Conclusion: This study shows that modern deep neural networks are capable of producing clinically applicable organ segmentation for the male pelvis. The model is able to produce acceptable structures as frequently as current clinical routine. Therefore, deep neural networks can simplify the clinical workflow by offering initial segmentations. The study further shows that to retain the clinicians' personal preferences a structure review and correction is necessary for structures both created by other clinicians and deep neural networks.

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better than Traditional Metrics in a Thoracic Cavity Segmentation Workflow

“…In thoracic cavity segmentations delineated on 329 CT datasets, we evaluated correlations between the time required to review and correct autosegmentations and eight spatial similarity metrics. We find the APL, FNPL, and surface DSC to be better correlates with correction times than traditional metrics, including the ubiquitous 4,6,10,11,16,[22][23][24][25][26]28,29,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] volumetric DSC. We find that clinical variables that worsen autosegmentation similarity to manually-corrected references do not necessarily prolong the time it takes to correct the autosegmentations.…”

“…14 Autosegmentations are useful if they obviate the need for a clinician to delineate segmentations de novo, which can be time-consuming 4,[15][16][17][18] and inconsistent [19][20][21][22][23] between observers and within the same observer at different time points. Several studies confirm that clinicians can save time from leveraging autosegmentation templates compared to de novo segmentation, 15,[24][25][26][27][28][29] but in many circumstances the time required for clinicians to review and correct autosegmentations is still substantial. For example, during online adaptive/stereotactic MRI-guided radiotherapy, 30 radiation oncologists must carefully correct cancer and normal anatomy autosegmentations while a patient waits immobilized in the treatment device.…”

“…Autosegmentations are usually compared with a reference segmentation by spatial similarity metrics that compare 1) volumetric overlap between an autosegmented structure and the same manually segmented structure, 4,6,10,11,16,[22][23][24][25][26]28,29,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] such as the volumetric Dice similiarity coefficient/index 48 (DSC), or 2) geometric distance between two structures' surfaces, 4,10,22,[24][25][26]28,[32][33][34][35]37,40,42,43,46,47 such as the Hausdorff distance 49 (HD), or 3) structure centricity, 32,39 such as differences between centers-of-mass. Critically, these metrics do not necessarily correlate with time savings in clinical practice.…”

International survey; current practice in On-line adaptive radiotherapy (ART) delivered using Magnetic Resonance Image (MRI) guidance

“…Bearing in mind contouring is the weakest link in the chain in the radiotherapy pathway, the best way forward to make the workflow more efficient whilst maintaining or improving quality must be considered. Artificial intelligence (AI) could have an important role here and is under intense investigation at present [24,25]. However, a human element will most probably remain, and the role and responsibilities will need to be defined.…”

Cross-Domain Data Augmentation for Deep-Learning-Based Male Pelvic Organ Segmentation in Cone Beam CT

“…It also involves data from an additional hospital and provides a more detailed discussion. Segmentation of male pelvic organs (bladder, rectum, prostate, and seminal vesicles) on CBCT and CT scans using a DL approach was the subject of a recent paper [25]. These authors' contribution consists mainly of the use of artificially-generated pseudo CBCT scans in the training set along with a high segmentation quality.…”