# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 4 Discussion
### Comparison between Deep Learning Models
The median size of the tumors in their dataset was 980 mm\({}^{3}\), while the smallest tumor was 3 mm\({}^{3}\). [PERSON] et al. (2019) [29] trained the convoluted neural network with multiplanar slices, yielding a dice score of 0.77. [PERSON] et al. (2021) [11] implemented an ensemble of 3D U-Net and DeepMedic and enhanced the prediction significantly, especially for non-experts. In their dataset, the median volume of the lesion was 890 mm\({}^{3}\). In our dataset, the lesions possessed a median size of 656 mm\({}^{3}\) and a mean of 2833 \(\pm\) 6389 mm\({}^{3}\), while the smallest lesion was 13.05 mm\({}^{3}\). Generally speaking, with the highest dice score of 0.51, sensitivity of 0.66, and precision of 0.48, the lesions in our dataset had a higher size variety and smaller median size. The inconsistency in the lesion characteristics could cause difficulties for the deep learning models to extract features and hinder the prediction. Ensemble models introduced higher segmentation performance than a single model in the previous studies [11; 27; 28; 29]. Although in our study, V-Net with a dropout rate of 0.1 outperformed other methods in segmentation of brain lesions in the NTUH dataset, we did not perform a benchmark on the ensemble methods. It remains undetermined whether ensemble models yield better performance as well as which models ensembled could enhance segmentation the most. As the difference in the imaging sequences used in the training process is a determinant of segmentation performance, the sequences used in previous works are discussed. [PERSON] et al. (2017) used contrast-enhanced T1-weighted images [27] while [PERSON] et al. (2019) used CT and T1-weighted MRI scans with contrast as the input [28]. Multiplanar slices of MPRAGE (magnetization-prepared rapid acquisition with gradient echo) images were taken as input in [PERSON] et al. (2019) [29]. [PERSON] et al. (2021) used contrast-enhanced CT and T1-weighted MR scans [11]. Out of the three studies, methods with MPRAGE as the input sequence yielded the highest dice score compared to the ground truth. Brain tumors on FLAIR, which is often used to contour the clinical target volume (CTV), mostly appeared as confluent hyperintense signals, introducing higher sensitivity and lower precision. On the other hand, brain tumors were mostly discrete on MPRAGE, an MRI modality taking advantage of gradient echo [58]. Despite the fact that higher precision could be achieved with MPRAGE, it is currently of lower significance in contouring before SRS. Of note, studies have shown that simultaneous use of different imaging modalities promised a better performance in segmentation compared to single modality use [38]. In our study, only contrast-enhanced T1-weighted MR images were used, and this could be a determinant of lower segmentation performance. The required dataset size to yield high performances could not be confirmed, as we collected the data available to train the models and only draw conclusions from the current dataset. It is probably true that a larger dataset may generate better or different results, but such a dataset was not available to us.