# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 1 Introduction  
Knee osteoarthritis is the most common musculoskeletal disease in the world [1]. Lesions commonly induce structural changes within the small articular cartilage [2]. MRI (magnetic resonance imaging) technologies provide the means to characterise structural alterations in different joint tissues affected by osteoarthritis. Patients who undergo knee MRI for presumed musculoskeletal disease can also have unexpected vascular findings or pathology in the imaged field. [3]. In general, osteoarthritis is categorised by the progressive degradation of joint tissues with various abnormalities [4] and has been a severe issue in recent years. However, some small anatomical structures around the joint are hardly detected. For example, the veins and ligaments can show critical early alarms in musculoskeletal lesions [5]. This study will explore the performance of small structure segmentation in knee MRI by using deep learning. The accurate segmentation of structures is helpful in clinical workflows in multiple domains such as diagnostic intervention and treatment planning. However, manual segmentation of anatomical structures is often time-consuming, labour-intensive, and prone to errors; therefore, it has prompted studies on automated segmentation [6]. The knee joint is one of the most important joints in the human body and is frequently injured in sports and other accidents. Automated knee segmentation can assist orthopaedists in examining and treating various kinds of knee lesions. Deep convolutional neural networks (CNNs) have been used for medical image segmentation since the rapid development of deeplearning techniques in recent decades. They have improved the segmentation accuracy, and decreased the time and manual labour. The definition of semantic segmentation is that each pixel or voxel of an image is marked with a specific label. A well-performed model can accurately classify each pixel or voxel. Traditionally, global features or structural features are beneficial for classification. They can be acquired through stacked convolutions with strides and various pooling operations, but the spatial information can be lost gradually during this process. Fully convolution networks (FCNs) [7] solved this problem, to some extent, by using three techniques: (1) replacing fully connected layers with convolutions to output images instead of probabilities; (2) using deconvolution to implement upsampling to reconstruct the output image; and (3) using skip connections to fuse information from layers with different strides to improve segmentation details. Medical image segmentation requires higher accuracy than natural image segmentation and requires neural networks to adjust the architecture to yield more precise segmentation. U-net [8] was developed based on FCN. It uses a symmetrical encoder-decoder architecture and has succeeded in medical image segmentation [9; 10]. In U-net, the encoder is a downsampling path that extracts features from input images through the combination of convolutions. The decoder uses upsampling operations instead of pooling, many stacked deconvolutions, and skip connections to combine the features with those in the encoder. These components enable the network to exploit multi-scale context information to obtain high-accuracy segmentation results. Most medical images are 3D, such as MRI and CT (computed tomography) [11]. 3D medical image segmentation is computationally expensive; therefore, systems are commonly trained patch-wise. When patch-wise training is adopted, the receptive field is reduced initially, which will hinder the ability to capture global features and larger context. To address this problem, we adopt networks that can gain the same receptive field with fewer parameters and enable us to use a larger patch size to feed the network. DeepLab series [12; 13; 14; 15] is designed for 2D image segmentation with high accuracy and detailed segmentation maps.