Deep Learning Auto-Segmentation on Multi-Sequence MRI for MR-Guided Adaptive Radiation Therapy

Abstract

<h3>Purpose/Objective(s)</h3> Robust auto-segmentation based on daily MRI is highly desirable for MR-guided adaptive radiation therapy (MRgART). However, it is generally challenging particularly if the daily MRI is acquired with multiple sequences or if it is for imaging complex anatomy, e.g., abdomen. To overcome these issues, we developed a deep learning algorithm to segment abdominal organs quickly and automatically on multi-sequence MRI. <h3>Materials/Methods</h3> The deep learning auto-segmentation model was developed using a modified U-net, ResUnet3D, with end-to-end mapping, and was trained with 85 daily MRI sets acquired during MRgART on a 1.5T MR-Linac for 37 patients with abdominal tumors. The daily MRI sets were motion averaged images derived from 4D MRI acquired with multiple sequences, including Fast Field Echo (FFE), Balanced Turbo Field Echo (BTFE) and TFE. Each daily MRI was paired with a CT. All MRIs were pre-processed using bias field correction and intensity standardization. The ground truth contours were created manually based on existing guidelines. For the training datasets, field-of-view extension via online random 3D elastic transform/deformation was utilized to augment and improve the quality of datasets. During the model training, a patient-based z-score normalization method was employed to standardize the multi-contrast images. Two models were trained, one for 11 organs: aorta, duodenum, kidneys, liver, pancreas, spinal cord, spleen, stomach, large bowel, and small bowel, and another for 10 organs with combined large and small bowel. Performance of the obtained models was evaluated using 11 test datasets in terms of dice similarity coefficient (DSC), mean distance to agreement (MDA) and time efficiency using an Intel Xeon CPU (2.4 GHz, 64GB RAM) hardware. <h3>Results</h3> For the challenging bowels, the auto-segmentation accuracy for the model trained with the combined bowel was better, with MDA/DSC of 2.1/0.86 versus 6.8/0.76 (large bowel) and 6.1/0.68 (small bowel) for the separate-bowel model. Average DSC for the obtained contours of aorta, left kidney, right kidney, liver, spinal cord, spleen, and stomach was 0.93, 0.93, 0.94, 0.94, 0.83, 0.92 and 0.88, respectively from both models. While no significant difference in model performance was observed with BTFE versus TFE testing datasets, a slightly improved segmentation was seen on the BTFE images for pancreas and duodenum, DSC 0.71 and 0.72. This improvement may be attributed to the enhanced contrast seen in BTFE images when compared to the TFE and FFE images. The time required to generate all organs on an MRI was 20-24 seconds. <h3>Conclusion</h3> We have developed deep learning auto-segmentation models based on multi-sequence MRI sets for abdominal organs. The models performed reasonably well. With further development using large datasets, the models may be integrated in MRgART for fast, fully automated, and accurate contouring of abdominal organs on daily MRI.