# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 4 Experiments and Results
### Discussion  
We trained U-net and U-Net + Res with dice loss as the preliminary model. Then, we proposed new varieties to discover the small and low-resourced pixels in the MRI dataset, i.e., the structures with SE block. The proposed varieties showed a satisfactory performance on small organ segmentation.  
For U-Net + Res trained with dice loss, from Table 3 we can see that some small anatomical structures such as PCL and ACL are not segmented correctly. For the U-net variant with residual SE blocks trained with weighted dice loss, the performances on the small structures are improved.  
The main question is that the neural networks have a smaller field of view on the resolution 400 \(\times\) 400 \(\times\) 400, although the patch sizes used on the two datasets are the same. The patch-wise training can be seen as reducing the receptive field of the images at the beginning. For the downsampling dataset, the view was reduced to \(\left(128\,/\,400\right)^{3}\;\approx\;0.033\). The locations of anatomical structures are in the centre of the image and will be separated into different patches; therefore, the difficulty of recognising their patterns increases.  
Tables 2 and 3 show the comparison of U-net variants and DeepLabv3 plus variants. DeepLabv3 plus variants enable us to segment all structures and achieve relatively higher average accuracy with dice loss. However, U-net variants cannot segment the small structures correctly, leading to lower average accuracy. Generally speaking, DeepLabv3 plus variants perform better than U-net variants in terms of average dice coefficient on all labels with dice loss.  
DeepLabv3 plus-based models are overall better than U-net-based models. Compared with natural images, the MR image semantics are relatively simple, and the pattern is relatively fixed; both high-level and low-level semantic features appear to be very important. To utilise the features, U-Net applied skip connection and U-shaped structure to combine the high-level and low-level features. However, this network becomes too complex, leading to more parameters in the model. Because it is tricky to obtain medical imaging data, many studies only provide data for less than 100 cases. Therefore, the model we designed should not be too large. Too many parameters can easily lead to overfitting.  
In U-net, the receptive field of pixels on the feature map depends on convolution and pooling operations. The receptive field of ordinary convolution can only increase two pixels at a time step, and the progress is too slow. The increase of the receptive field of the traditional convolutional network is generally done by pooling operation. The pooling operation will increase the receptive field while reducing the image's resolution, thus losing some information. Moreover, the upsampling of the pooled image will make it impossible to restore a lot of detailed information, limiting segmentation accuracy.  
To address the problem, DeepLabv3 plus utilises a new atrous convolution. It was performed in the ASPP structure (see Figure 3), which simultaneously satisfies:  
Figure 6: (**a**) Performance matrix of U-Net-based model; (**b**) performance matrix of DeepLabv3 plus-based model.  
Connecting high- and low- MRI features together;
2. Significantly reducing model parameters, and thus alleviating overfitting and shortening training time.  
As a result, in our experiments, DeepLabv3 plus variants achieve the best performance on average accuracy. However, the performance could be improved further by adopting more advanced components. These components should be able to obtain a larger receptive field with fewer parameters and have the ability to reserve details during the process.