| dc.description.abstract | Bone-subtraction is an image processing technique that is often used in clinical settings to enhance computed tomography angiography (CTA) interpretation. With the newly developed deep learning technology, bone-subtraction CTA can potentially be further improved by adopting deep convolutional neural network (CNN) models implemented with advanced graphic processing units (GPU). Supervised learning has been proven to be an effective approach to creating artificial intelligence for medical image analysis, where the quality of labeled datasets plays the essential role in the speed of model convergence. However, a major drawback of most existing approaches in labeling medical images is that domain expert knowledge is required. Further, the extremely high time cost for labeling “big data” has made three dimensional (3D) medical data labeling a challenging task. Labeling vessels in CTA is difficult to achieve in practice due to shortage of qualified experts and the intrinsic difficulty in annotating arterial and venous vessels in 3D data. In contrast, labeling bones is a more feasible approach. In this study, we propose a cylindrical sampling strategy to assist non-experts in labeling mandible and spine for computed tomography (CT) scans. It takes advantage of the quasi-anatomical symmetry in respective to the body’s longitudinal axis. This cylindrical sampling approach allows generating 2D resampled scans with pan anatomical landmarks which are derived from the continuum of anatomical structures by rotating different angles along the longitudinal axis. Twenty computed tomography angiography datasets were included in this study. C1, C2 spinal bones, and mandibles were manually or automatically labeled by using the proposed cylindrical resampling scans. The labeling time for each bone was recorded and compared. After the image labeling process, we also conducted an experiment in comparing the convergency of the proposed cylindrical scans with traditional (transverse, sagittal, and coronal) scans using a deep learning semantic segmentation Unet model with an FPN-ResNeXt backbone. Fifteen subjects (75%) were randomly selected as the training set, and the remaining 5 subjects (25%) were used as the test set. The model was trained with 1, 5, 10, and 15 subjects respectively. The experiment was repeated for 5 times. In total, the model failed to converge 1 (5%), 12 (60%), 1 (5%), and 5 (25%) times for cylindrical, transverse, sagittal, and coronal scans respectively. The cylindrical approach generated the highest test F1-scores of 91.3%, 92.8%, and 93.8% in C1, C2, and mandible segmentation respectively. The time used in labeling cylindrical and transverse mandible scans was 90 and 190 minutes per subject respectively. The experimental results show that the proposed cylindrical sampling method for head and neck CTA not only reduces the labeling time but also achieves better segmentation of bones. | en_US |