nnUNet semi-supervised (trained w/ external public dataset [ProstateX])

About

Creator:

krgz.ahmet1

Contact email:

krgz.ahmet1@gmail.com

Image Version:

a4d009a4-e0a3-4421-9141-a0437b12e677

Last updated:

Oct. 15, 2022, 8:05 a.m.

Associated publications:

Cuocolo R, Comelli A, Stefano A, et al.. Deep Learning Whole‐Gland and Zonal Prostate Segmentation on a Public MRI Dataset. Magnetic Resonance Imaging. 2021;54(2):452-459.

Armato SG, Huisman H, Drukker K, et al.. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imag. 2018;5(04):1.

Interfaces

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

Inputs Outputs

	Inputs	Outputs
1	Coronal T2 Prostate MRI Description Coronal T2 MRI of the Prostate Kind Image Read from `/input/images/coronal-t2-prostate-mri/<uuid>.mha` or `/input/images/coronal-t2-prostate-mri/<uuid>.tif` Coronal T2 Prostate MRI Transverse T2 Prostate MRI Description Transverse T2 MRI of the Prostate Kind Image Read from `/input/images/transverse-t2-prostate-mri/<uuid>.mha` or `/input/images/transverse-t2-prostate-mri/<uuid>.tif` Transverse T2 Prostate MRI Sagittal T2 Prostate MRI Description Sagittal T2 MRI of the Prostate Kind Image Read from `/input/images/sagittal-t2-prostate-mri/<uuid>.mha` or `/input/images/sagittal-t2-prostate-mri/<uuid>.tif` Sagittal T2 Prostate MRI Transverse HBV Prostate MRI Description Transverse High B-Value Prostate MRI Kind Image Read from `/input/images/transverse-hbv-prostate-mri/<uuid>.mha` or `/input/images/transverse-hbv-prostate-mri/<uuid>.tif` Transverse HBV Prostate MRI Transverse ADC Prostate MRI Description Transverse Apparent Diffusion Coefficient Prostate MRI Kind Image Read from `/input/images/transverse-adc-prostate-mri/<uuid>.mha` or `/input/images/transverse-adc-prostate-mri/<uuid>.tif` Transverse ADC Prostate MRI Clinical Information Prostate MRI Description Clinical information to support clinically significant prostate cancer detection in prostate MRI. Provided information: patient age in years at the time of examination (patient_age), PSA level in ng/mL as reported (PSA_report or PSA), PSA density in ng/mL^2 as reported (PSAD_report), prostate volume as reported (prostate_volume_report), prostate volume derived from automatic whole-gland segmentation (prostate_volume_automatic), scanner manufacturer (scanner_manufacturer), scanner model name (scanner_model_name), diffusion b-value of (calculated) high b-value diffusion map (diffusion_high_bvalue), Malignant Neoplasm Histotype (histology_type), Prostate Imaging-Reporting and Data System (PIRADS), Neural invasion (neural_invasion, yes/no), Vascular invasion (vascular_invasion, yes/no), Lymphatic invasion (lymphatic_invasion, yes/no). Values acquired from radiology reports will be missing, if not reported. Kind Anything Read from `/input/clinical-information-prostate-mri.json` Example { "PSA_report": 9.3, "PSAD_report": 0.465, "patient_age": 76, "scanner_model_name": "Ingenia", "scanner_manufacturer": "Philips Medical Systems", "diffusion_high_bvalue": 2000, "prostate_volume_report": 20.0 } Clinical Information Prostate MRI	Case-level Cancer Likelihood Prostate MRI Description Case-level likelihood of harboring clinically significant prostate cancer, in range [0,1]. Kind Float Write to `/output/cspca-case-level-likelihood.json` Example 42.0 Case-level Cancer Likelihood Prostate MRI Transverse Cancer Detection Map Prostate MRI Description Single-class, detection map of clinically significant prostate cancer lesions in 3D, where each voxel represents a floating point in range [0,1]. Kind Heat Map Write to `/output/images/cspca-detection-map/<uuid>.mha` or `/output/images/cspca-detection-map/<uuid>.tif` Transverse Cancer Detection Map Prostate MRI

Challenge Performance

Date	Challenge	Phase	Rank
Jan. 22, 2025	PI-CAI	Open Development Phase - Tuning	36

Model Facts

Summary

Additional dataset was used (ProstateX dataset).

Mechanism

Additional dataset was used (ProstateX dataset).

Validation and Performance

Left empty by the Algorithm Editors

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

Left empty by the Algorithm Editors

Common Error Messages

Left empty by the Algorithm Editors

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