A Generalizable Guided Deep Learning Auto-Segmentation Method of Pancreatic GTV on Multi-Protocol Daily MRIs for MR-Guided Adaptive Radiotherapy

Abstract

Fast and consistent delineation of gross-tumor volume (GTV) on non-contrast enhanced daily MRI is critical but can be challenging in MR-guided Online Adaptive Radiation Therapy (MRgOART). Our previous works have demonstrated the potential of using deep learning (DL) method for pancreatic GTV segmentation on MRI of a single sequence. However, the application of the obtained model is limited by the variations of MRIs from different scan protocols. To overcome the protocol dependence, we propose a generalizable prior knowledge guided DL method for pancreatic GTV auto-segmentation using the guidance of patient-specific contour from the verification MRI (obtained in the treatment room before the first fraction) on subsequent multi-protocol daily MRIs.The daily MRIs acquired from 21 pancreatic cancer patients on a 1.5T MR-Linac during MRgOART were used to demonstrate the method. Two different scan protocols were used: T2 TSE sequence for nine patients and 4D Vane sequence for the remaining twelve patients. Manual contours carefully delineated on each MRI by treating physicians were utilized as ground truth. All the MRIs were pre-processed (e.g., bias corrected, normalized, cropped, and resampled). Each daily MRI was rigidly registered to the verification MRI and a total of 99 image pairs were generated. The DL network backbone used was UNet with 3 encoders interconnected with 3 decoders and a classification block. Model input was the composite of verification MRI, verification GTV mask, and daily MRI. Data augmentation methods such as translation, rotation, flip, guidance-target inversion, and rigid registration error introduction were applied for robust model training. Patient-based leave-one-out-cross-validation was employed and the model performance was evaluated using Dice similarity coefficient (DSC) compared with the ground truth.The average DSC was 0.63 ± 0.18 for all the cases studied. For the cases with GTV > 5cc, the average DSC was 0.76 ± 0.13 and 0.69 ± 0.08 for T2 TSE and 4D Vane MRIs, respectively. The results are reasonably comparable to the inter-observer variation of DSC = 0.71 estimated in a previous study. The time required to generate GTV contour on a daily MRI set was less than 1s on a hardware with an I7 CPU and a GTX 1060 GPU.The proposed prior-knowledge guided deep leaning auto-segmentation method for pancreatic GTV on daily multi-protocol non-contrast MRIs performed reasonably well even with the relatively small dataset. This proof-of-principle study suggests that, with patient-specific prior knowledge guidance, it is feasible to implement a single DL auto-segmentation model for multiple MRI sequences in MRgOART. The method can be generalized for other structures or tumors. (First and second authors contributed equally.).