Hevi AI Prostate Zonal Segmentation

About
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:
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Results: Prostate zonal segmentation (segmentation), Softmax prostate peripheral zone segmentation (probabilistic map), Softmax prostate central zone segmentation (probabilistic map)
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Source code: https://github.com/ahmetkrgztr/HeviAI_picai/tree/main/AbdomenMRUS-prostate-segmentation
Interfaces
This algorithm implements all of the following input-output combinations:
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Validation and Performance
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.