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Abstract #5157

Transfer Learning for Segmentation of the Whole Prostate and Intraprostatic Lesions in Multi-Parametric MRI

Arham Ali1, Laxmi Muralidharan1, Shonit Punwani2, Adam Retter2, and Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom

Synopsis

Keywords: Diagnosis/Prediction, Segmentation

Motivation: Public challenge datasets are beneficial to improve classification and segmentation by computer aided diagnosis (CAD); transfer learning may further improve CAD clinical performance.

Goal(s): To investigate if transfer learning can improve the accuracy of whole-prostate and lesion segmentation in multi-parametric MRI.

Approach: Two nnU-Net networks for whole-prostate and lesion segmentation were initially trained on the PROSTATEx public dataset, then fine-tuned with an in-house clinical dataset.

Results: Fine-tuning on clinical data improved the mean Dice score for whole-prostate segmentation from 0.783 to 0.898. However, lesion segmentation networks underperformed due to dataset variability, indicating that while transfer learning is promising, lesion segmentation needs further refinement.

Impact: A nnU-Net network trained on large public datasets, then fine-tuned with a small clinical dataset improved whole-prostate segmentation. This network will facilitate processing requiring whole-prostate masks, such as Quantitative Susceptibility Mapping, and could potentially reduce radiological workload or automate quantification.

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Keywords