Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation

Abstract

Accurate and robust segmentation of lung cancers from CTs is needed to more accurately plan and deliver radiotherapy and to measure treatment response. This is particularly difficult for tumors located close to mediastium, due to low soft-tissue contrast. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby a 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 student segmentation networks. Our framework can be combined with any I2I and segmentation network. We demonstrate our framework's feasibility using 3 segmentation and 2 I2I methods. All networks were trained with 377 CT and 82 T2w MRI from different sets of patients. Ablation tests and different strategies for incorporating MRI information into CT were performed. Accuracy was measured using Dice similarity (DSC), surface Dice (sDSC), and Hausdorff distance at the 95 th percentile (HD95). The CMEDL approach was significantly (p < 0.001) more accurate than non-CMEDL methods, quantitatively and visually. It produced the highest segmentation accuracy (sDSC of 0.83 ± 0.16 and HD95 of 5.20 ± 6.86mm). CMEDL was also more accurate than using either pMRI's or the combination of CT's with pMRI's for segmentation.