Abstract #0094

Multi-contrast MR-driven deep learning for abdominal multi-organ segmentation (McDAMOS)

Pengcheng Wang^1,2, Dan Ruan^3,4, Junzhou Chen^1,4, Jiayu Xiao¹, Diane Ling⁵, Lijun Ma⁵, Wensha Yang⁶, and Zhaoyang Fan^1,2,5

¹Department of Radiology, University of Southern California, Los Angeles, CA, United States, ²Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ³Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ⁵Department of Radiation Oncology, University of Southern California, Los Angeles, CA, United States, ⁶6. Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Analysis/Processing, Segmentation

Motivation: Efficient and accurate contouring of abdominal organs-at-risk (OAR) is crucial for MR-guided radiotherapy planning and online adaptation but challenging due to complex anatomy. Multi-contrast MR may be utilized to achieve automated multi-organ segmentation.

Goal(s): To develop a multi-contrast MR-driven DL technique for abdominal multi-organ segmentation.

Approach: Our model builds on a 3D Swin Transformer architecture with T1w and T2w dual inputs. Pre-training on a larger T1w dataset and synthesized T2w images addressed limited data. A VAE-based loss for organ shape learning was incorporated.

Results: Multi-contrast inputs, pre-training, and VAE loss all contributed to improved segmentation performance, especially for challenging organs like the duodenum.

Impact: Our work demonstrates the utility of multi-contrast MR in achieving abdominal auto-segmentation and presents a methodology to address limited data available from a novel research MR sequence. The approach benefits clinicians and propelling automated segmentation techniques forward.

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