# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 2 Related Works  
Machine learning techniques are widely applied in recent medical applications, such as disease detection [19; 20] and medical robots [21]. However, typical data-driven models perform differently under diverse requirements [22; 23; 24]. For medical image segmentation, researchers proposed dozens of neural networks with encoder-decoder architectures, such as SegNet [25], RefineNet [26], and DecovNet [27]. The architectures mainly consisted of an encoder, a decoder, and fusion techniques. The encoder is responsible for extracting features from input images, where the dimension of feature maps is reduced. It can be seen as a classification neural network without fully connected layers. For example, SegNet uses VGG16 [28] in the encoder, DeepLabv3 plus uses improved Xception [29] as a part of its encoder. In the decoder, the spatial dimension of feature maps is recovered through various upsampling operations, which is the opposite of the encoder. The third important part is the fusion technique, which can utilise multi-scale features from the encoder to recover the spatial resolution in the decoder. For example, U-net uses skip connection to fuse the features in the encoder with the features in the decoder. The fusion of features in different scales is beneficial in improving the accuracy of semantic segmentation. DeepLabv3 [14] discussed four types of FCNs to capture multi-scale context, including image pyramid, encoder-decoder, context module (e.g., using atrous convolution as an approach), and spatial pyramid pooling. The first one is usually applied during the inference stage [30]. For the other three approaches, DeepLabv3 plus takes advantage of them to propose a network that employs the architecture of encoder-decoder with atrous separable convolution. ASPP (atrous spatial pyramid pooling) is one of the most important techniques used in the DeepLab series. It was proposed in DeepLabv2 [13] and developed based on spatial pyramid pooling, which was proposed by [31] to capture the context of images in different strides. DeepLabv2 employs a combination of spatial pyramid pooling with atrous convolution and named it ASPP, which is computationally efficient compared with the original method. DeepLabv3 improves ASPP by adding \(1\times 1\) convolution in the first layer and global average pooling in the last layer. In addition, DeepLabv3 plus exploits atrous depthwise separable convolution to replace atrous convolution, which reduces the number of parameters to implement ASPP again. DeepLabv3 plus is designed for 2D natural image segmentation. In this work, we modified the ASPP technique to 3D image segmentation. 3D image segmentation is very computationally expensive, and therefore related techniques that can make it more efficient are highly appreciated. Due to limited computing resources, volumetric inputs are usually cut into patches to feed them into 3D networks, enabling the use of a large input image size to feed the networks if the network can be simplified. Other methods to decrease the computing resource are involved. For example, a 3D volume image is comprised of 2D slices, so researchers such as [32] attempted to use 2D networks to segment 2D slices and then fuse the results into a 3D volume image again. However, there are spatial information losses that have an adverse influence on volume image segmentation results. In addition, there are multiple labels in our datasets, including large structures and small structures. FocusNet [33] proposes a method that uses different networks to segment large structures and small structures, fusing the results to form the final segmentation and achieve high segmentation accuracy. Researchers have proposed several MRI segmentation models aimed at various tissues for knee joint treatment. Reference [34] applied using U-net for capturing the complex morphology and texture of thigh muscle and adipose tissue. The results showed a good clinical effect compared with contralateral knees without knee pain and comparable effect size to manual segmentation.