Synopsis

Currently, many studies exploit deep learning and mp-MRI data to enhance the diagnostic accuracy of prostate cancer characterisation. In this study, we focus on VERDICT DW-MRI data and compare its diagnostic performance to those of the ADC map and the raw DW-MRI from the mp-MRI. Specifically, we compare the performance obtained by a fully convolutional neural network (CNN) when training and test is performed on the raw VERDICT DW-MRI, the ADC maps and the DW-MRI data from the mp-MRI acquisition. The results indicate that the CNN performs better when it is trained and tested on VERDICT DW-MRI.

Introduction

Methods

Data acquisition

We acquired data from 18 patients on a 3T scanner (Achieva, Philips Healthcare, NL). All the patients underwent a standard mp-MRI [4] supplemented by VERDICT DW-MRI. VERDICT DW-MRI data was acquired with a pulsed-gradient spin-echo (PGSE) sequence using the optimised imaging protocol for VERDICT prostate characterisation with 5 b-values (90-3000 $\mathrm{s/mm^2}$) in 3 orthogonal directions [5]. Also, images with b = 0 $\mathrm{s/mm^2}$ were acquired before each b-value acquisition. Regarding the DW-MRI data from the mp-MRI acquisition, 4 b-values were acquired (0, 150, 500, 1000 $\mathrm{s/mm^2}$), and subsequently, the ADC map was calculated by the scanner software. Two board certified radiologists contoured malignant and benign regions of interest (ROIs) on the registered VERDICT DW-MRI data and the ADC map.

Data analysis

We perform pixel-wise classification on malignant and benign ROIs on the VERDICT DW-MRI data, the ADC map and mp-MRI DW-MRI data using a convolutional encoder-decoder architecture. We consider two different classes (malignant, benign) and train the network using pixel-wise cross entropy loss

$\mathrm{CE= \sum_j l_j log(p_j) + (1-l_j) log(1-p_j)},$

where $\mathrm{p_j}$ is the probability that pixel $\mathrm{j}$ belongs to class 1 and $\mathrm{l_j}$ is the true label of pixel $\mathrm{j}$.

We use an encoder-decoder architecture (MRI-UNet) which is a modified version of the U-Net architecture [6] (Figure 2). MRI-UNet has fewer convolutional layers to avoid overfitting and has the following form: $\mathrm{\text{C}_8\text{-P-C}_{16} \text{-P-C}_{32}\text{-P-C}_{32}\text{-TC}_{32}\text{-C}_{32}\text{-TC}_{16}\text{-C}_{16}\text{-TC}_8\text{-C}_8\text{-C}_2}$, where $\mathrm{C_N}$ is a convolutional layer with N 3x3 filters, P is a 2x2 max-pooling layer and $\mathrm{TC_K}$ is a transposed convolutional layer with K 2x2 filters. Each convolutional layer is followed by batch normalisation (BN) [7] and a rectified-linear unit (ReLU) [8]. We implement the networks using Pytorch [9]. We employ a 10-fold cross validation approach to train and test the networks. We repeat each 10-fold cross validation 5 times and report the average performance. We train the networks for 200 epochs and select the model which has the smallest loss on a validation set (30% of the training set). We use stochastic gradient descent (SGD) with a mini-batch size of 32, a constant learning rate of 1e-5, a momentum of 0.9 and a weight decay of 1e-3.

Results

We train and test the network on labelled malignant and benign ROIs. Figure 3 shows the receiver operating characteristic (ROC) curves of MRI-UNet when training and test is performed on the raw VERDICT DW-MRI data, the ADC map and the raw DW-MRI data from the mp-MRI acquisition. The results show that MRI-UNet achieves better performance (an area under the curve (AUC) of 92.40%), when it is trained and tested on VERDICT DW-MRI data. When the network is trained and tested on the ADC map and the raw DW-MRI data from the mp-MRI acquisition, it achieves an AUC of 86.07% and 86.94% respectively.

Discussion

Acknowledgements

References

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