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TRUVACE RECORD VERSION
record: TRV-2026-0969
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-03T06:03:13.230062Z
status: published
lens: g_space
sector: health
headline: A polyline searching-driven evolutionary AI for disease detection of medical imaging data
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.

To address these issues, our method introduces a novel coarse-to-fine optimization framework to overcome these challenges on ultrasound data, which comprises four main stages: (1) initial region of interest (ROI) localization using a deep learning model; (2) vertex sequence determination through a principal curve-based polyline search; (3) optimal initialization of the backpropagation neural network (NN) via an enhanced quantum evolutionary algorithm; (4) defining the ROI boundary with a mathematical model based on NN parameters. 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.
limitation: 
tag: Evidence-backed gain
key_points: 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.
rundown: 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. To address these issues, our method introduces a novel coarse-to-fine optimization framework to overcome these challenges on ultrasound data, which comprises four main stages: (1) initial region of interest (ROI) localization using a deep learning model; (2) vertex sequence determination through a principal curve-based polyline search; (3) optimal initialization of the backpropagation neural network (NN) via an enhanced quantum evolutionary algorithm; (4) defining the ROI boundary with a mathematical model based on NN parameters.
sources:
- peer_reviewed | Physics in Medicine & Biology | https://doi.org/10.1088/1361-6560/ae95ac | 2026-09-02
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