Object detection as an aid for locating the prostate in surface-based abdominal ultrasound images.

Rory D Bennett, Tristan Barrett, Vincent J Gnanapragasam, Zion Tsz Ho Tse

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In one paragraph

Article in Communications engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Rory D BennettSchool of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.

Tristan BarrettDepartment of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK.

Vincent J GnanapragasamDepartment of Surgery, University of Cambridge School of Clinical Medicine, CB2 0QQ, Cambridge, UK.

Zion Tsz Ho TseSchool of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK. z.tse@qmul.ac.uk.ORCID http://orcid.org/0000-0001-9741-1137

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automatic object detection is increasingly used in the medical field to enhance clinical workflows before, during, and after diagnosis of various conditions. One example is prostate detection and prostate volume estimation, which can aid in triaging patients for prostate cancer through risk-stratification using prostate-specific antigen density. In this paper, a baseline prostate detection framework is presented, highlighting that current state-of-the-art object detection models can detect the prostate in difficult to interpret surface-based ultrasound images. A 5-fold cross-validation study returned intersection-over-union, precision, recall, F1, and average-precision values above

Identifiers

PMID41315641

DOI10.1038/s44172-025-00550-y

PMCPMC12728191

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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.