Publications

Authors:

Cianflone, Francesco; Maris, Bogdan; Bertolo, Riccardo; Veccia, Alessandro; Artoni, Francesco; Pettenuzzo, Greta; Montanaro, Francesca; Porcaro, Antonio Benito; Bianchi, Alberto; Malandra, Sarah; Ditonno, Francesco; Cerruto, Maria Angela; Zamboni, Giulia; Fiorini, Paolo; Antonelli, Alessandro

Title:

Development of Artificial Intelligence-Based Real-Time Automatic Fusion of Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasonography of the Prostate

Year:

2025

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

UROLOGY

ISSN of journal:

0090-4295

N° Volume:

199

Number or Folder:

May

Page numbers:

27-34

Keyword:

AI Artificial Intelligence; prostate cancer; biopsy; image guided surgery; software

Short description of contents:

Objectives: To report the development of artificial intelligence (AI)-based software to allow for the autonomous fusion of transrectal ultrasound and multiparametric magnetic resonance images of the prostate to be used during transperineal prostate biopsies. Materials and methods: This study was approved by the Institutional Review Board (protocol ID3167CESC). The automatic software development for fusion biopsy involved three steps: 1) Developing an AI component to segment the prostate during ultrasound; 2) Developing the component to segment anatomical structures in magnetic resonance images using labeled datasets from the Cancer Imaging Archive and in-house scans; 3) Developing the fusion component to register segmented ultrasound and magnetic resonance images using a three-step process: pre-alignment, rigid alignment, and elastic fusion, validated by measuring the lesion distance between modalities. Statistical analysis included descriptive statistics and the Mann-Whitney U test, evaluating outcomes with Dice scores and average precision metrics. Results: The ultrasound component showed a Dice score of 0.87 with a test set of 75,357 images and 28,946 annotations. The magnetic resonance component achieved a Dice score of 0.85 on a test set of 2,494 images and annotations. It also demonstrated a mean Average Precision of 0.80 for bounding boxes and 0.88 for segmented objects, both measured at a 50% intersection over union threshold. The fusion AI component reduced the median magnetic resonance-ultrasound lesion distance from 8 mm (IQR 6-9) after rigid fusion to 4 mm (IQR 3-5) after elastic fusion (p<0.001). Conclusion: A data processing pipeline and AI were created to allow for the autonomous fusion of ultrasound and magnetic resonance images to be ideally used during transperineal prostate biopsies.

Note:

Epub 2025 Mar 11

Product ID:

144728

Handle IRIS:

11562/1157633

Last Modified:

May 29, 2025

Bibliographic citation:

Cianflone, Francesco; Maris, Bogdan; Bertolo, Riccardo; Veccia, Alessandro; Artoni, Francesco; Pettenuzzo, Greta; Montanaro, Francesca; Porcaro, Antonio Benito; Bianchi, Alberto; Malandra, Sarah; Ditonno, Francesco; Cerruto, Maria Angela; Zamboni, Giulia; Fiorini, Paolo; Antonelli, Alessandro, Development of Artificial Intelligence-Based Real-Time Automatic Fusion of Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasonography of the Prostate «UROLOGY» , vol. 199 , n. May , 2025 , pp. 27-34

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo