Population-Scale Segmentation of Penile Tissue in DIXON MRI using Deep Learning for Quantitative Phenotyping in Male Reproductive Health
arXiv:2607.02127v1 Announce Type: cross Abstract: Penile measurement is clinically relevant across male reproductive and urogenital health, including conditions such as micropenis, congenital and endocrine disorders, and sexual or urinary dysfunction. However, quantitative assessment of penile size has relied mainly on external length or circumference measurements, which are difficult to standardize, sensitive to measurement conditions, and unable to capture the internal portion of the penis. MRI enables volumetric assessment of the whole penis in vivo, but automated segmentation has not previ
Advances in deep learning and medical imaging, specifically MRI, are converging to enable detailed anatomical analysis that was previously impossible or highly subjective.
This development allows for standardized, quantitative assessment of internal anatomy, improving diagnosis and treatment efficacy in male reproductive health, and sets a precedent for similar applications in other complex body regions.
The ability to accurately and automatically segment penile tissue from MRI data shifts penile measurement from subjective external assessments to objective, volumetric internal analyses, reducing diagnostic variability.
- · Urology and Andrology clinics
- · Medical imaging software developers
- · Patients with male reproductive health issues
- · AI in healthcare companies
- · Traditional manual image analysis methods
- · Manufacturers of less precise diagnostic tools
Automated, precise anatomical measurements will improve diagnostic accuracy and treatment planning for various male reproductive and urogenital conditions.
The standardization of such quantitative metrics could lead to new research avenues, clinical guidelines, and drug development targeting specific penile pathologies.
This precision phenotyping might eventually reduce unnecessary invasive procedures or failed treatments by better tailoring interventions based on internal anatomical detail.
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Read at arXiv cs.LG