# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 1 Introduction
DeepLabv1 [12] introduces atrous convolution and fully connected conditional random fields (CRF) to solve the problems brought by reduced resolution. DeepLabv2 [13] proposes atrous spatial pyramid pooling (ASPP), which uses multiple parallel atrous convolution layers with different sampling rates. DeepLabv3 [14] discusses four types of FCNs and improved ASPP. DeepLabv3 plus [15] improves ASPP with atrous depthwise separable convolution to get the same receptive field with fewer parameters than standard convolutions. It uses an asymmetrical encoder-decoder architecture. The encoder is comprised of improved Xception and ASPP. The decoder uses bilinear upsampling concatenated with the corresponding low-level features from the network backbone. Medical datasets are usually imbalanced, which will reduce the performance of machine learning models [16; 17; 18]. For the annotated MRI knee images used in this work, there are multiple classes where the frequencies of voxels of each class are extraordinarily different. For example, the size of bone is much larger than blood vessel (see Section 4). When the networks with fewer parameters are selected, their representation capacity may not be sufficient to maintain the required details to obtain accurate segmentation results for small objects/organs. However, patch-wise training cannot be avoided if complicated networks are chosen, damaging captured structural features that are beneficial for the segmentation of larger structures. For example, bones will be carelessly separated into different patches, making it difficult for the model to learn their structural features. Therefore, structural features need to be better extracted to assist the segmentation. We need to consider the results between the required segmentation details of both small and large structures and the computational cost. This paper proposes different U-net variants to check the performance between diverse knee anatomical structures. Inspired by DeepLabv3 plus, we further develop DeepLabv3 plus with ASPP (atrous spatial pyramid pooling). We test the effectiveness of DeepLabv3 plus variants to balance the capability of global feature extraction and requiredresolution. As a result, DeepLabv3 plus variants perform better than U-net variants in terms of the average dice coefficient of all structures. The main contributions include:  
1. We propose four types of 3D U-net variants aimed at small anatomical structure segmentation in MRI images. We found that SE block performs well in small anatomical detection;
2. Based on the success of SE block in U-net variants, we apply the DeepLabv3 plus with SE block and transfer the 2D DeepLabv3 plus into a 3D version for anatomical segmentation of real MRI images;
3. In experiments, we improve the results from the small anatomical structure segmentation on the knee MRI images provided by Sunnmere MR-Klinikk. Based on the experiments, it is concluded that DeepLabv3 plus variants could achieve relatively high segmentation accuracy of small structures without decreasing accuracy for large structures. The rest of this paper is organised as follows: Section 2 introduces the related works on image segmentation. Section 3 describes our applied neural networks, U-net and the recent version of DeepLab. Section 4 discuss the experimental results on the MRI dataset. The conclusions are described in Section 5.