Vanya V Valindria1, Saurabh Singh2, Eleni Chiou1, Thomy Mertzanidou1, Baris Kanber1, Shonit Punwani2, Marco Palombo1, and Eleftheria Panagiotaki1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom
We present non-invasive Gleason Score (GS) classification using VERDICT-MRI with convolutional neural networks. Results show that the combination of VERDICT maps achieves the best GS classification performance and outperforms reported multi-parametric MRI GS classification.
Figure 1. Flowchart of Gleason score classification. We classify the predefined lesion ROIs on the VERDICT maps using DenseNet and SE-ResNet. The network then gives the corresponding Gleason score to the lesion.
Figure 2. Results of five-point GS classification using two different networks: DenseNet and SE-ResNet on VERDICT. SE-ResNet with VERDICT generally yields better performance than DenseNet. VERDICT gives higher classification metrics compared to those reported in GS classification with bi-and multi-parametric MRI.