Evidence map›Paper›PMID 40421308›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2025

MINIMALLY USER-GUIDED 3D MICRO-ULTRASOUND PROSTATE SEGMENTATION.

Alex Ling Yu Hung, Kai Zhao, Kaifeng Pang, Qi Miao, Zhaozhi Wang, Wayne Brisbane, Demetri Terzopoulos, Kyunghyun Sung

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alex Ling Yu HungUniversity of California, Los Angeles, CA 90095, USA.
Kai ZhaoUniversity of California, Los Angeles, CA 90095, USA.
Kaifeng PangUniversity of California, Los Angeles, CA 90095, USA.
Qi MiaoUniversity of California, Los Angeles, CA 90095, USA.
Zhaozhi WangUniversity of California, Los Angeles, CA 90095, USA.
Wayne BrisbaneUniversity of California, Los Angeles, CA 90095, USA.
Demetri TerzopoulosUniversity of California, Los Angeles, CA 90095, USA.
Kyunghyun SungUniversity of California, Los Angeles, CA 90095, USA.

Funding

Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer ClassificationR01CA248506 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUNG, KYUNG HYUN, WU, HOLDEN H · 2020 to 2024
$3.0M
Racially-associated MRI analysis and modeling for predicting aggressive prostate cancerR01CA272702 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Harrison Kim, Kyung Hyun Sung · 2023 to 2026
$2.4M
NCI NIH HHS R01 CA248506NCI NIH HHS R01 CA272702
6 · The paper itself

Abstract

Micro-ultrasound is an emerging imaging tool that complements MRI in detecting prostate cancer by offering high-resolution imaging at lower cost. However, reliable annotations for micro-ultrasound data remain challenging due to the limited availability of experts and a steep learning curve. To address the clear clinical need, we propose a click-based, user-guided volumetric micro-ultrasound prostate segmentation model requiring minimal user intervention and training data. Our model predicts the segmentation of the entire prostate volume after users place a few points on the two boundary image slices of the prostate. Experiments show that the model needs only a small amount of training data to achieve strong segmentation performance, with each of its components contributing to its overall improvement. We demonstrate that the level of expertise of the user scarcely affects performance. This makes prostate segmentation practically feasible for general users.

Indexed as

micro-ultrasound imagingprostate segmentationuser-guided segmentation

Identifiers

PMID40421308
PMCPMC12104093

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.