Unpaired Cross-Modality Educed Distillation (CMEDL) for Medical Image Segmentation.

ABSTRACT: Accurate and robust segmentation of lung cancers from CT, even those located close to mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure treatment response. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby an informative teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background. Our contribution eliminates two requirements of distillation methods: (i) paired image sets by using an image to image (I2I) translation and (ii) pre-training of the teacher network with a large training set by using concurrent training of all networks. Our framework uses an end-to-end trained unpaired I2I translation, teacher, and stud

SUBMITTER: Jiang J 

PROVIDER: S-EPMC9128665 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature