# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 4 Discussion  
### Segmentation Performance: NTUH vs. BraTS Dataset  
The performance on our radiosurgery dataset was inferior to that on BraTS. Many factors might lead to such a result. First of all, the tumor volumes in the NTUH dataset are typically smaller than those in BraTS 2015. On average, the tumor occupied 1.23% of the whole image volume in the BraTS dataset, but only 0.145% in ours. It should also be noted that a significant portion of our dataset contained multiple targets, which is much less likely for glioma patients (BraTS). The lesions in NTUH dataset are thus more difficult to detect. Moreover, there is significant heterogeneity in our dataset. To evaluate whether our model could achieve similar segmentation performance under a more realistic scenario, we used the dataset containing cranial lesions of various pathology, which is different from the BraTS dataset with only glioma cases. In a strict sense, we also have some images of non-neoplastic diseases such as AVM. Additionally, some of the tumors are extra-axial (outside the brain parenchyma) and may even extend extracranially, so we cannot perform skull stripping like BraTS. Due to the heterogeneity of tumor types and sites, we may need a much larger dataset to reach similar performance. Our results indicated that better performance was correlated with more training samples (as in metastases, meningioma, and schwannoma, Figure 2) and larger lesion dimensions (Figure 3). We also report the effect of input channels (of BraTS) in this revision. Another reason is that we only used one image set (T1+C) to predict instead of four sequences used in the BraTS dataset. Less information might lead to deteriorated performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  &
\begin{tabular}{c} **V-Net** \\ **Dropout 0.1** \\ \end{tabular} & **DeconvNet** & **U-Net** & **PSPNet** & **DeepMedic** \\ \hline Inference time & & & & & \\ (minutes:seconds) & 02:51 & 04:00 & 04:01 & 14:43 & 17:73 \\ Number of & & & & & \\ parameters & 8.23M & 12.5M & 34.5M & 28.3M & 13M \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Inference time on our hardware and parameters of different networks. Figure 3: The performance of each model trained with four-channel or one-channel inputs. It is also worth mentioning that our dataset is quite imbalanced disease-wise. From the performance of the models we trained, we could observe that this imbalance resulted in serious bias issues for minority patients. We found it quite difficult to train a model by the traditional soft-dice loss or cross-entropy loss. Using the weighted cross-entropy loss gave us a 0.25 dice score, while our modification of subtracting a log-soft-dice term improved the dice score to 0.40. Such difference may result from tumor size since tumors in our dataset were of fewer voxels on average. In addition to the data variety, the weighted cross-entropy function could be very unstable and thus harmful to the optimization. Empirically, we found that the model will most likely fail in 10 epochs and predict nothing but the background for all inputs. By adding another term with the dice score, the new loss function provides better guidance to the model, and we could empirically observe the significant improvements. We added images of trigeminal neuralgia in the training set as negative samples, in which there was no real space-occupying lesion. We did not expect the machine to learn how to identify trigeminal neuralgia. Instead, it can be considered that images of trigeminal neuralgia are examples of the heterogeneity of real clinical datasets. This artificial impurity was meant to mimic the systematic bias that could occur in a larger and unpurified dataset to infer the availability of deep learning models. Although the targets in our dataset were defined and contoured by experienced clinicians, it should be noted that they were the targets we wanted to treat. Therefore, in very few cases, not every lesion detected by human experts was labeled.