Hevi AI Prostate Zonal Segmentation

About

Creators:

krgz.ahmet1

joeran.bosma

Contact email:

krgz.ahmet@hotmail.com

Image Version:

fc63b5a0-0757-422a-9f58-a0c6b4fdced7

Last updated:

Feb. 26, 2023, 12:29 p.m.

Associated publication:

Karagoz A, Seker ME, Yergin M, et al.. Prostate Lesion Estimation using Prostate Masks from Biparametric MRI. arXiv. Published online January 25, 2023.

Interfaces

This algorithm implements all of the following input-output combinations:

	Inputs	Outputs
1	Transverse T2 Prostate MRI (Image)	Prostate Zonal Segmentation (Segmentation) Softmax Prostate Peripheral Zone Segmentation (Heat Map) Softmax Prostate Central Gland Segmentation (Heat Map)

Model Facts

Summary

This model segments the central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). The model requires a NIFTI MR image as input and creates a probability map and segmentation mask as output. The model is based on nnUnet.

Mechanism

This algorithm is a deep learning-based model (nnU-Net model). We trained these models with a total of 204 prostate T2 MRI scans paired with a manual prostate segmentation. These scans were sourced from ProstateX. Annotations for ProstateX cases were retrieved from https://github.com/rcuocolo/PROSTATEx_masks/.

Input:

Datatype: Transverse T2 MR image of the prostate
File format: NIFTI image
Target group: Male patients

Output:

Results: Prostate zonal segmentation (segmentation), Softmax prostate peripheral zone segmentation (probabilistic map), Softmax prostate central zone segmentation (probabilistic map)
Source code: https://github.com/ahmetkrgztr/HeviAI_picai/tree/main/AbdomenMRUS-prostate-segmentation

Validation and Performance

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Uses and Directions

This algorithm was developed for research purposes only. This algorithm is intended to be used only on prostate T2 MRI examinations.
This model is intended to be used by radiologists for predicting prostate volume in biparametric MRI examinations. The model is not meant to guide or drive clinical care.

Warnings

Even if used appropriately, clinicians using this model can estimate prostate volume incorrectly.
This model is not designed to guide clinical diagnosis and treatment for prostate cancer.

Common Error Messages

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Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3