Machine learning for contour classification in TG-263 noncompliant databases.

A large volume of medical data are labeled using nonstandardized nomenclature. Although efforts have been made by the American Association of Physicists in Medicine (AAPM) to standardize nomenclature through Task Group 263 (TG-263), there remain noncompliant databases. This work aims to create an algorithm that can analyze anatomical contours in patients with head and neck cancer and classify them into TG-263 compliant nomenclature. To create an accurate algorithm capable of such classification, a combined approaching using both binary images of individual slices of anatomical contours themselves, as well as center of mass coordinates of the structures are input into a neural network. The center of mass coordinates were scaled using two normalization schemes, a simple linear normalization scheme agnostic of the patient anatomy, and an anatomical normalization scheme dependent on patient anatomy. The results of all of the individual slice classifications are then aggregated into a single classification by means of a voting algorithm. The total classification accuracy of the final algorithms was 97.6% mean accuracy per class for nonanatomically normalization scheme, and 97.9% mean accuracy per class for anatomically normalization scheme. The total accuracy was 99.0% (13 errors in 1302 structures) for the nonanatomically normalization scheme, and 98.3% (22 errors in 1302 structures) for the anatomically normalization scheme.

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detection of PCa will use an engineered scalar image called Ktrans to summarize the information in the DCE time-series images. This work proposes a new model that amalgamates the U-net and the convGRU neural network architectures for the purpose of interpreting DCE time-series in a temporal and spatial basis for segmenting PCa in MR images. Ultimately, experiments show that the proposed model using the DCE time-series images can outperform a baseline U-net segmentation model using Ktrans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model.