Brain metastases detection and segmentation on magnetic resonance images is laborious, error-prone and often irreproducible for radiologists and radiation oncologist. We present a specific deep slice-crossed network with local weighted loss to automatically detect and segment brain metastases on contrast-enhanced T1WI images. The results demonstrated the good performance, high robustness and generalizability of the model. In addition, compared with radiologists, the model showed higher sensitivity and increased efficiency in identifying and segmenting brain metastases. The results jointly suggested that the proposed model is a promising tool to assist the workflow in the clinical practice.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords