Abstract #0421

Computer-aided detection and segmentation of brain metastases in MRI for stereotactic radiosurgery via a deep learning ensemble

Zijian Zhou¹, Jeremiah W. Sanders¹, Jason M. Johnson², Tina M. Briere³, Mark D. Pagel⁴, Jing Li⁵, and Jingfei Ma¹

¹Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ³Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁴Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Manual delineation of brain metastases for stereotactic radiosurgery (SRS) is time consuming and labor intensive. We successfully constructed a deep learning ensemble, including a single shot detector and U-Net, to detect and subsequently segment brain metastases in MRI for SRS treatment planning. Postcontrast 3D T1-weighted gradient echo MR images from 266 patients were randomly split by 212:54 for model training-validation and testing. For the testing group, an overall sensitivity of 80.4% (189/235 metastases) with 4 false positives per patient, and a median segmentation Dice of 77.9% (61.4% - 86.3%) for the detected metastases were achieved.

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