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Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation

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Abstract

Objective

To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation.

Methods

The algorithm, combining model-based and deep learning–based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm’s mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression.

Results

Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm’s median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists’ delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation.

Conclusions

The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging.

Key Points

• Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma).

• The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks.

• The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.

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Abbreviations

ABD:

Average boundary distance

AFMS:

Anterior fibromuscular stroma

CG:

Central gland

DNN:

Deep neural network

DSC:

Dice similarity coefficient

GE:

General Electric

HD:

Hausdorff distance

HD95:

95Th percentile of the Hausdorff distance

MR:

Magnetic resonance

MRI:

Magnetic resonance imaging

PZ:

Peripheral zone

R1:

Radiologist 1

R2:

Radiologist 2

RVD:

Relative volume difference

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Funding

This research project was sponsored and funded by the Hospices Civils de Lyon and performed under the framework of the collaboration between the Hospices Civils de Lyon and Philips that is part of the GOPI public contract whose holder is Philips.

Author information

Authors and Affiliations

  1. Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d’Arsonval, F-69437, Lyon, France

    Olivier Rouvière, Paul Cezar Moldovan & Sylvain Gouttard

  2. Université de Lyon, F-69003, Lyon, France

    Olivier Rouvière, Marc Colombel & Muriel Rabilloud

  3. Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France

    Olivier Rouvière, Marc Colombel & Muriel Rabilloud

  4. INSERM, LabTau, U1032, Lyon, France

    Olivier Rouvière

  5. Philips France, 33 rue de Verdun, CS 60 055, 92156, Suresnes Cedex, France

    Anna Vlachomitrou

  6. Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France

    Benjamin Riche & Muriel Rabilloud

  7. Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France

    Benjamin Riche & Muriel Rabilloud

  8. Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany

    Alexandra Groth & Juergen Weese

  9. Philips, MATAM Industrial Park, 3508409, Haifa, Israel

    Mark Rabotnikov

  10. Department of Urology, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, F-69310, Pierre-Bénite, France

    Alain Ruffion

  11. Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France

    Marc Colombel & Sébastien Crouzet

Authors
  1. Olivier Rouvière
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  2. Paul Cezar Moldovan
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  4. Sylvain Gouttard
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  5. Benjamin Riche
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  6. Alexandra Groth
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  7. Mark Rabotnikov
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  8. Alain Ruffion
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  9. Marc Colombel
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  10. Sébastien Crouzet
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  11. Juergen Weese
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  12. Muriel Rabilloud
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Corresponding author

Correspondence to Olivier Rouvière.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Prof. Olivier Rouvière.

Conflict of interest

Four co-authors (AV, AG, MaR, JW) are Philips employees. No author has financial conflict of interest regarding the products assessed in this study.

Statistics and biometry

Two of the authors have significant statistical expertise.

Informed consent

Training cases were selected from a prospectively maintained database (CLARA-P database). The creation of this database was approved by the Comité de Protection des Personnes Sud-Est IV and included patients provided written informed consent for the use of their MR images and histological results for research purposes.

Test cases were randomly selected among all the prostate MRIs in our PACS. According to the French law, retrospective analysis of these routine cases was approved by our Ethics Committee. In addition, all patients whose MRI was selected as a test case received a letter explaining the study and giving them the possibility to withdraw.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some of the training MRIs (included in the CLARA-P database) have been used in other studies:

Bratan F et al., Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study. Eur Radiol 2013; 23:2019.

Vaché T et al., Characterization of prostate lesions as benign or malignant at multiparametric MR imaging: comparison of three scoring systems in patients treated with radical prostatectomy. Radiology 2014; 272:446.

Niaf E et al., Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging–influence of a computer-aided diagnosis system. Radiology 2014; 271:761.

Bratan F et al., How accurate is multiparametric MR imaging in evaluation of prostate cancer volume? Radiology 2015; 275:144.

Hoang Dinh A et al., Quantitative analysis of prostate multiparametric MR images for detection of aggressive prostate cancer in the peripheral zone: a multiple imager study Radiology 2016; 280:117.

None of these studies assessed automated prostate zonal segmentation.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Rouvière, O., Moldovan, P.C., Vlachomitrou, A. et al. Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation. Eur Radiol 32, 3248–3259 (2022). https://doi.org/10.1007/s00330-021-08408-5

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  • DOI: https://doi.org/10.1007/s00330-021-08408-5

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