Background and objective: Lower back pain in humans has become a major risk. Classical approaches follow a non-invasive imaging technique for the assessment of spinal intervertebral disc (IVDs) abnormalities, where identification and segmentation of discs are done separately, making it a time-consuming phenomenon. This necessitates designing a robust automated and simultaneous IVDs identification and segmentation of multi-modality MRI images.

Methods: We introduced a novel deep neural network architecture coined as ‘RIMNet’, a Region-to-Image Matching Network model, capable of performing an automated and simultaneous IVDs identification and segmentation of MRI images. The multi-modal input data is being fed to the network with a dropout strategy, by randomly disabling modalities in mini-batches. The performance accuracy as a function of the testing dataset was determined. The execution of the deep neural network model was evaluated by computing the IVDs Identification Accuracy, Dice coefficient, MDOC, Average Symmetric Surface Distance, Jaccard Coefficient, Hausdorff Distance and F1 Score.

Results:Proposed model has attained 94% identification accuracy, dice coefficient value of $91.7\pm 1\%$ in segmentation and MDOC $90.2\pm 1\%$. Our model also achieved $0.87\pm 0.02$ for Jaccard Coefficient, $0.54\pm 0.04$ for ASD and $0.62\pm 0.02$ mm Hausdorff Distance. The results have been validated and compared with other methodologies on dataset of MICCAI IVD 2018 challenge.

Conclusions: Our proposed deep-learning methodology is capable of performing simultaneous identification and segmentation on IVDs MRI images of the human spine with high accuracy.

背景与目的：下腰痛已成为人类的主要风险。经典方法遵循非侵入性成像技术来评估脊椎椎间盘（IVD）异常，其中椎间盘的识别和分割是分开进行的，这使其成为一种耗时的现象。这就需要设计一个强大的自动和同时进行的IVD识别和多模态MRI图像的分割。

方法：我们引入了一种新颖的深度神经网络架构，称为“ RIMNet”，这是一种区域到图像匹配网络模型，能够执行自动和同时的IVD识别和MRI图像分割。通过随机禁用小批处理中的模式，将多模式输入数据通过一种丢弃策略提供给网络。确定了作为测试数据集的函数的性能精度。通过计算IVD识别精度，骰子系数，MDOC，平均对称表面距离，Jaccard系数，Hausdorff距离和F1得分，评估了深度神经网络模型的执行情况。

结果：提出的模型具有94％的识别精度，骰子系数值为$91.7\pm \mathrm{1个}％$ 在细分和MDOC中 $90.2\pm \mathrm{1个}％$。我们的模型也实现了$0.87\pm 0.02$ 对于Jaccard系数， $0.54\pm 0.04$ 对于ASD和 $0.62\pm 0.02$ 毫米Hausdorff距离。结果已得到验证，并与MICCAI IVD 2018挑战数据集上的其他方法进行了比较。

结论：我们提出的深度学习方法能够对人脊柱的IVD MRI图像进行高精度的同时识别和分割。