Abstract #4732

Fully Automatic Learning-based Multi-Organ Segmentation(ALMO) in abdominal MRI for Radiotherapy Planning using Deep Neural Networks

Yuhua Chen^1,2, Yujin Xie³, Lixia Wang^1,4, Jiayu Xiao¹, Zixin Deng¹, Yi Lao⁵, Richard Tuli⁵, Debiao Li¹, Wensha Yang⁵, and Zhaoyang Fan¹

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Biomedical Engineering, UCLA, Los Angeles, CA, United States, ³Beihang University, Beijing, China, ⁴Beijing Chaoyang Hospital, Capital Medical University, Beijing, China, ⁵Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Precise dose measurement is critical in radiotherapy planning, which involves accurate and fast segmentation of the organ for estimation of the region at risk. Segmentation Magnetic Resonance Imaging (MRI), as it is gaining more favor against CT in radio therapy, is new for multi-organ segmentation task. In this work, we proposed a fast, accurate, and fully automatic technique (ALMO) that reliefs the intense human labor from manual segmentation in a timing fashion. On our 51-subject dataset, our proposed method achieves an average dice score of 0.76 in the test set in seconds.

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