  Accurate localization and segmentation of intervertebral disc (IVD) is
crucial for the assessment of spine disease diagnosis. Despite the
technological advances in medical imaging, IVD localization and segmentation
are still manually performed, which is time-consuming and prone to errors. If,
in addition, multi-modal imaging is considered, the burden imposed on disease
assessments increases substantially. In this paper, we propose an architecture
for IVD localization and segmentation in multi-modal MRI, which extends the
well-known UNet. Compared to single images, multi-modal data brings
complementary information, contributing to better data representation and
discriminative power. Our contributions are three-fold. First, how to
effectively integrate and fully leverage multi-modal data remains almost
unexplored. In this work, each MRI modality is processed in a different path to
better exploit their unique information. Second, inspired by HyperDenseNet, the
network is densely-connected both within each path and across different paths,
granting the model the freedom to learn where and how the different modalities
should be processed and combined. Third, we improved standard U-Net modules by
extending inception modules with two dilated convolutions blocks of different
scale, which helps handling multi-scale context. We report experiments over the
data set of the public MICCAI 2018 Challenge on Automatic Intervertebral Disc
Localization and Segmentation, with 13 multi-modal MRI images used for training
and 3 for validation. We trained IVD-Net on an NVidia TITAN XP GPU with 16 GBs
RAM, using ADAM as optimizer and a learning rate of 10e-5 during 200 epochs.
Training took about 5 hours, and segmentation of a whole volume about 2-3
seconds, on average. Several baselines, with different multi-modal fusion
strategies, were used to demonstrate the effectiveness of the proposed
architecture.
