Alana Heafala, Lauren Ball, Sharyn Rundle-Thiele, Lana J Mitchell
Through education, we can make change': A design thinking approach to entry-level dietetics education regarding eating disorders
Nutrition & dietetics: the journal of the Dietitians Association of Australia
automatic abdominal organ segmentation essential prerequisite accurate volumetric analysis disease diagnosis tracking medical practitioners however deformable shapes variable locations overlapping nearby organs similar contrast make segmentation challenging moreover requirement large manually labeled dataset makes harder hence semisupervised contrastive learning approach utilized perform automatic abdominal organ segmentation existing 3d deep learning models based contrastive learning able capture 3d context medical volumetric data along three planesviews axial sagittal coronal views work semisupervised viewadaptive unified model vaumodel proposed make 3d deep learning model viewadaptive learn 3d context along view unified manner method utilizes novel optimization function assists 3d model learn 3d context volumetric medical data along view single model effectiveness proposed approach validated three types datasets btcv nih msd quantitatively qualitatively results demonstrate vau model achieves average dice score 8161 389 improvement compared previous best results pancreas segmentation multiorgan dataset btcv also achieves average dice score 7776 7676 pancreas single organ nonpathological nih dataset pathological msd dataset