A deep learning model leveraging SAM improved segmentation speed and matched radiologist performance in classifying ovarian lesions on MRI.
Key Details
- 1Researchers from Johns Hopkins developed an MRI-based, end-to-end DL pipeline incorporating Meta’s Segment Anything Model (SAM) and DenseNet-121.
- 2Integrated model reduced segmentation time by 4 minutes per lesion compared to manual segmentation.
- 3SAM achieved a Dice coefficient of 0.86 to 0.88 for lesion segmentation.
- 4DL model achieved an AUC of 0.85 internally and 0.79 externally, on par with radiologists’ AUC of 0.84 (p > 0.05).
- 5Training data included 534 lesions (internal) and 87 lesions (external) from the US and Taiwan.
Why It Matters
This study demonstrates that foundation AI models can efficiently streamlines lesion segmentation and classification with accuracy rivaling radiologists, suggesting a path toward more automated and collaborative clinical workflows in radiology.
Source
AuntMinnie
Related News
•Radiology Business
UC Irvine Implements AI to Find Lung Cancer Screening Candidates
UC Irvine's radiology department developed an NLP tool to identify patients eligible for lung cancer screening from EHR notes.
•Radiology Business
Webinar to Showcase Successful AI Automation in Radiology Workflows
A webinar will provide insights from imaging leaders on expanding and optimizing AI-driven automation in radiology clinical workflows.
•Radiology Business
A Practical Guide to Evaluating Radiology AI Solutions
Experts propose a three-step approach for radiologists to assess and select AI imaging solutions.