# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 4 Discussion
### Comparison between Deep Learning Models  
With respect to the input format, there are two classes of model architectures. The 2D model predicts tumors in just one slice and completely discards the information along the \(z\)-axis, while the 3D model utilizes the full information on the MRI volume. This results in a trade-off between features and overfitting. When receiving more features, it is more likely to overfit the unrelated noise, especially with such a small dataset. Patch sizes in previous works range from 16 \(\times\) 16 \(\times\) 16 to 64 \(\times\) 64 \(\times\) 64 mm\({}^{3}\)[18; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55], of which [PERSON] et al. outperformed the others. Thus, in the current proposed work, we restrained the receptive field and predicted on inputs patches with the size of 64 \(\times\) 64 \(\times\) 64 mm\({}^{3}\). We examinedthis trade-off in our benchmark experiment on the BraTS dataset. Surprisingly, when experimenting with V-Net on our dataset, small patch-wise prediction became detrimental, but receiving the full brain volume guaranteed the best performance. Overall, the 3D models seem to be more appealing. The 3D models present the full potential of convolution networks, reducing the number of parameters and becoming far more efficient due to their convolution nature. Specifically, V-Net has approximately 1/30 of the parameters compared to U-Net, shortest inference time, and the best performance on dice metric. The only shortcoming of 3D models is the requirement of GPU RAM due to the large input. In our experiments, we solve this by using a smaller batch-size. Furthermore, replacing batch normalization with dropout is quite effective in preventing overfitting because of the small batch size. We compared the performances of the models trained with one-channel inputs of the NTUH and BraTS datasets. When the models were trained with one-channel inputs, the segmentation performances were slightly better than when they were trained with four-channel inputs. It could be inferred that the models perform better on a dataset with less heterogeneity in lesion types as well as lesion sizes. Comparison to Previous Studies Addressing Deep Learning-Based Segmentation in SRS Treatment Planning  
Efforts to identify the targets and the OARs prior to SRS treatment are crucial for dosimetry planning to protect the organs other than the lesions themselves. Several studies have benefited from deep learning methods on the classification and nomenclature standardization of the OARs [56; 57]. The above-mentioned studies could advance computer-assisted radiation therapy. To evaluate the benchmark performed in this study on the segmentation of brain lesions, previous studies addressing the segmentation of brain tumors in the treatment planning process during SRS will be reviewed. Of all types of brain lesions, asymptomatic or unresectable metastases warrant SRS without maximal surgical resection. As SRS serves as the first-line treatment for oligometastatic lesions, which denotes metastases of lesser than five lesions, contouring the lesions is of important clinical significance. The models previously used included modified DeepMedic [11; 27], an ensemble of DeepMedic and 3D U-Net [28], and CNN [29]. Tumor volume tremendously affects the performance of segmentation; higher variety in tumor sizes and smaller lesions usually imply adversity in segmentation. Smaller lesions, while not affecting dice scores much, are not easily detected in methods with lower sensitivity. [PERSON] et al. (2017) [27] proposed a modification of DeepMedic and managed to reach a dice score of 0.67. In their study, the number of brain metastases per case varied from 1 to 93 (5.679 \(\pm\) 8.917), and the mean tumor size was 672 \(\pm\) 1994 mm\({}^{3}\). [PERSON] et al. (2019) [28] ensembled two neural networks, namely 3D U-Net and DeepMedic, yielding a good performance in segmentation with a median dice score of 0.74.