Meeting Banner
Abstract #4732

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

Yuhua Chen1,2, Yujin Xie3, Lixia Wang1,4, 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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here