Adversarial Training of Deep Convolutional Neural Network for Multi-organ Segmentation from Multi-sequence MRI of the Abdomen

Automated delineation of organs play a vital role during the treatment planning for clinical practitioner. With magnetic resonance (MR) images being widely used in radio-therapy, the organ delineation from multi-sequence MR images has become more desirable. Segmenting abdominal organs from MR images is critical in surgical planning. However, the organs segmentation from MR volume is a challenging task, as there are different visual appearances for the same organ in the different sequences of MR images. Recently, convolutional neural network (CNN) methods have achieved promising results in automating organ delineation. However, the performance of deep learning (DL) to emerging approaches such as adversarial learning in the field of segmentation are rarely explored. In this paper, we have trained an encoder-decoder like architecture as a segmentation network, using segmentation loss minimization along with training it to adversarial attack another CNN implementing the visual Turing test discriminator. This way of adversarial training has shown significant improvement in performance. We have used the CHAOS-19 dataset of abdominal MRI for segmentation of the liver, left kidney, right kidney, and spleen. We have used Dice coefficient (DC) to measure the performance, and achieved an average DC of 0.97 with the faster convergence.