## 1 Introduction  
Low back pain is a common disease in modern society. It can be caused by disorders of lumbar components such as an intervertebral disc, paraspinal muscle, and vertebral body. Therefore, it is important to examine the specific components of the lumbar spine for accurate diagnosis and treatment. Assessment of the intervertebral disc is particularly important since its shape is liable to physiological (age-related) and pathological changes [1; 2]. Magnetic resonance (MR) imaging is a very effective non-invasive imaging modality for obtaining such information. However, segmentation of intervertebral discs in MR spine images is typically challenging for the following reasons: (1) object shapes are deformed and rotated; (2) the contrast between an object and its surroundings can be very low, which renders the boundary unclear; (3) the intensity within an object is not uniform. Segmentation of intervertebral discs in MR spine images has been extensively studied. [PERSON] et al. [3] studied the application of graph-cut method for intervertebral disc segmentation and [PERSON] et al. [4] sought to detect and segment intervertebral discs using atlas-based and fuzzy clustering methods. [PERSON] et al. [5] proposed a detection and segmentation method for intervertebral discs using anisotropic oriented flux, while [PERSON] et al. [6] proposed a 3D intervertebral disc segmentation algorithm using a simplex active surface model using weak shape prior. However, performance of these conventional methods, which depend on mathematical algorithms with hand-crafted features, is limited by the challenges mentioned above. Recent years have witnessed remarkable advances in the field of machine learning, especially with the use of deep-learning techniques. Convolutional neural networks (CNNs) effectively extract image features and perform effective classification based on these features. Several intelligent techniques, such as computer aided diagnoses that employ CNNs, have been reported in the field of medical imaging [7]. [PERSON] et al. [8] attempted segmentation of intervertebral discs in MR spine images using a classification network by splitting the entire image into small patches. The most common and effective CNN in medical image segmentation is the U-network (U-net) proposed by [PERSON] et al. [9]. As shown in Figure 1, a U-net is composed of an encoding part and a decoding part. The encoding part of conventional U-net is composed of convolutional layers and pooling layers and the decoding part is composed of convolutional layers and up-convolutional layers. Conventional U-net performs efficient feature extraction and segmentation using a large receptive field obtained through this structure [8]. However, since conventional U-net is based on feature extraction network for image classification, information pertaining to fine details of the image may disappear during the pooling process in the encoding part. For example, max-pooling layers, which is commonly used in U-nets, retains a pixel with the largest value among the neighboring four pixels and removes the information of the other pixels. Therefore, the pooling layer helps to efficiently detect the dominant information representing image characteristics, albeit with a loss of detailed information. The missing detail is not restored during up-convolutional layers. A skip connection can be added to this network to overcome this problem; however, it cannot completely recover the finer details. As a result, low-frequency information of the image is generally emphasized [10; 11]. Figure 2 displays a comparison between the results of the conventional U-net segmentation and manually segmented labels. Dice similarity coefficient (DSC) [12] of segmentation for a whole area of intervertebral discs is 87.49%, while the DSC at the boundaries of the discs is as low as 40.87%. This suggests that it is difficult to achieve fine grain segmentation with conventional U-net and it may lead to unsatisfactory results for complex objects, such as intervertebral discs.