aceA polyline searching-driven evolutionary AI for disease detection of medical imaging data
Objective. Accurate Ultrasound (US) prostate cancer (PCa) segmentation images hold significant value for organ interventional guidance and clinical disease diagnosis. However, this task still poses substantial challenges. The main obstacles include blurred or incomplete boundaries separating PCa from adjacent soft tissues, shadow artifacts inherent to ultrasound imaging, and drastic inter-patient variations in organ morphological shapes. Approach . To address these issues, our method introduces a novel coarse-to…
Our method outperforms current algorithms in ultrasound PCa data, achieving mean Dice similarity coefficient (DSC), Jaccard similarity coefficient (OMG), and accuracy (ACC) of 83.6 ± 3.1%, 71.8 ± 2.5%, and 83.5 ± 3.1%, respectively.
Evidence
- Peer-reviewedPhysics in Medicine & Biology2026-09-02
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Truvace Impact Record TRV-2026-0969, v1: “A polyline searching-driven evolutionary AI for disease detection of medical imaging data.” Truvace, 2026-09-03. /record/TRV-2026-0969 (accessed at citation time). sha256 9c3069427671cca8…
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