Medical imaging and computer technologies have revolutionized healthcare by improving diagnostic accuracy and increasing patient safety and comfort by making massive amounts of medical images available. Deep learning methods perform well in computer vision when labeled training data is abundant. In the practice of medical imaging, the labeling or otherwise segmentation of images is performed manually. However, manual medical image segmentation has two significant drawbacks: long delineation times and questionable reproducibility. To address this issue, we developed an automated intervertebral disc instance segmentation approach that can use T1 and T2 images during this study to address data limitation issues and computational time issues and improve the algorithm's generalization. We proposed a Multi Mix Mask-RCNN (M3RCNN) for deep learning segmentation networks based on Mask-RCNN. Our method used a mixed optimization and training data system, employing Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam) with T1 and T2. Compared to segmentation methods that were commonly used in the past, the proposed method significantly improved both processing time and segmentation results.