Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data

ProCAncer-I Consortium, Nuno Miguel Rodrigues, José Guilherme de Almeida, Ana Sofia Castro Verde, Ana Mascarenhas Gaivão, Carlos Bilreiro, Inês Santiago, Joana Ip, Sara Belião, Raquel Moreno, Celso Matos, Leonardo Vanneschi, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Sara Silva, Nickolas Papanikolaou

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Abstract

Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.
Original languageEnglish
Article number108216
Pages (from-to)1-22
Number of pages22
JournalComputers in Biology and Medicine
Volume171
DOIs
Publication statusPublished - 1 Mar 2024

Keywords

  • ProstateNet
  • Prostate segmentation
  • Lesion segmentation
  • Zone segmentation

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