# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 4 Discussion
### Limitation of This Study  
Compared to previous works investigating samples that underwent SRS, a relatively large dataset was implemented in the current study. However, the results suggest that the numbers of pituitary tumors, AVMs, and other tumors are probably insufficient for good results. Since the numbers of above lesions in a single institute may be insufficient, federated learning can be a potentially practical approach for better results.  
Contrast-enhanced T1-weighted MR imaging was the only modality used as input in our study. Some tumors such as low-grade glioma or pituitary tumors are non-enhancing, introducing great difficulty in the detection and segmentation of these types of lesions. Simultaneous use of multiple imaging modalities could be the solution to this. Reviewing previous works, the sensitivity for detection of smaller brain lesions (<3 mm) with 3D U-Net, whether trained with black-blood or gradient echo modalities, decreased significantly compared to larger brain lesions (\(\geq\)10 mm, 0.981, 3-10 mm 0.829, <3 mm 0.235) [59]. The same trend could be observed in studies performed with 2-stage MetNet (\(\geq\)6 mm 0.99, 3-6 mm 0.87, \(\leq\)3 mm 0.25) [60] or GoogLeNet [61]. The 2-stage MetNet [60] and BMDs net [62] could achieve satisfactory segmentation prediction on tumors larger than 6 mm, with dice scores of 0.87 and 0.83, respectively. In our dataset counterpart, the diameters of 10.5% lesions were smaller than 6 mm, 45% lesions smaller than 10 mm, and 95.7% smaller than 3 cm. The small lesion sizes in our NTUH dataset contributed to the dice score lower than 0.6 predicted with V-Net.  
The way dice score is derived could mask the effect of contouring small lesions. In our work, dice score was calculated per voxel, which favored larger lesions compared to dice score derived per lesion. Clinically, SRS is indicated and is of significant importance for patients with smaller brain lesions, whereas for patients who are surgical candidates with larger lesions, standard care remains surgery with adjuvant stereotactic radiation therapy or whole-brain radiation therapy. As for patients with diffuse lesions, whole brain radiation therapy is the standard treatment due to the lack of level 1 evidence to support the use of SRS in the patient population [63] (p. 865). Contouring deflection on the gross tumor volume (GTV) of such small lesions could introduce a huge impact on later target contouring, compromising organs at risk (OAR). Take brain metastases, for example, current guidelines for contouring for SRS generally indicate a 1.5 cm expansion from GTV to generate CTV. In our dataset, the smallest volume of brain lesion being 20 mm\({}^{3}\) implies a 3.4 mm diameter, and the volume difference of CTV with GTV is about 3000 mm\({}^{3}\). This expansion in target volume significantly differs if a small lesion was not correctly contoured. As a consequence, a dice score per lesion provides benefit in some circumstances.  
Evidence derived from trials concerning treatment response to SRS based on either deep-learning segmentation or manual segmentation is still an unmet need. Several studies implemented multiple modalities (PET/MRI) in order to train machine learning models for tumor segmentation, which suggested that biological target volume (BTV) could be promising in helping CTV definition during SRS treatment and their ability to indicate dose escalation on biologically active targets [64; 65]. Despite the effort in assisting CTV definition by taking advantage of the training set of multi-modalities, whether the addition in modalities to either of the learning methods improves clinical treatment response is yet undetermined.