Abstract #0662

Deep Learning-based Automatic Detection and Segmentation of Brain Metastases Using Multi-Task Learning with 3D Black-Blood and GRE Imaging

Yohan Jun*¹, Yae Won Park*², Yangho Lee¹, Kyunghwa Han², Chansik An³, Seung-Koo Lee², Sung Soo Ahn**², and Dosik Hwang**¹

¹Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ²Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea, Republic of, ³Research and Analysis Team, National Health Insurance Service Ilsan Hospital, Goyang, Korea, Republic of

For the detection of brain metastases, either contrast-enhanced 3D gradient echo (GRE) or spin echo (SE) imaging with black-blood (BB) imaging techniques are commonly used. The objective of this study was to evaluate whether a deep learning (DL) model using both 3D BB imaging and 3D GRE imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging. We demonstrated that the combined 3D BB and 3D GRE DL model can improve the detection and segmentation performance of brain metastases, especially in detecting small metastases.

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