# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 1 Introduction  
Stereotactic radiosurgery (SRS) is a treatment modality using ionizing radiation, focusing on precisely selected areas of tissue. It is usually delivered in a single session, but the radiation dose can also be fractionated. Targeting accuracy and anatomic precision are critical to successful SRS, but are historically secondary concerns in other types of radiation therapy [1]. Undoubtedly, as technology evolves, standards in this area will have to change. Nevertheless, when root mean square errors can be reduced to approximately 1 mm, a threshold of surgical possibilities is reached both in the brain and throughout the rest ofthe body. As the ACR-ASTRO guidelines suggest, a targeting accuracy is approximately 1 mm [2; 3; 4]. Although SRS can be performed in many parts of the body, it is best known to treat intracranial lesions. The common indications for intracranial SRS include many different types of brain tumors, vascular malformations (including arteriovenous malformation, AVM), and functional diseases such as trigeminal neuralgia (TN). Brain metastases, vestibular schwannomas, meningiomas, and pituitary adenomas are common tumor types treated by SRS. Before the delivery of SRS to the target (e.g., a brain tumor), detailed treatment planning with precise contouring of the target is conducted by a neurosurgeon or a radiation oncologist. The contouring is performed on computed tomography (CT) or magnetic resonance images (MRI). Sometimes, both CT and MRI are used, depending on the devices and diseases. Normal organs or tissues sensitive to radiation are also contoured so that radiation dose and risk of injury can be estimated. These normal organs are called critical organs or organs at risk (OARs). In terms of image analysis, \"precise\" segmentation of targets and OARs is mandatory for SRS treatment planning. In current clinical practice, the segmentation is performed by professional personnel. The manual contouring process is time-consuming and prone to substantial inter-practitioner variability, even amongst experts, and may lead to large variation in care quality. Several pieces of research suggest computer assistance [5; 6; 7; 8; 9; 10]. We expect an AI-based assistive tool could improve tumor detection, shorten mean contouring time, and increase inter-clinician agreement [11]. As convolutional neural networks (CNNs), the dominant deep learning models, are leading the breakthrough in computer vision recently, they also dominate MRI segmentation tasks. [PERSON] et al. (2017) proposed the idea of using a deep learning model to perform brain tumor segmentation tasks on MRI images [12]. They pointed out that both local and global representations are essential to produce better results, and this intuition was later realized in various ways. [PERSON] et al. (2017) later perfected this idea and achieved state-of-the-art performance with a two-path model [13]. On the other hand, U-Net was first proposed for the cell tracking task [14], but then became widely used in many other segmentation tasks [15; 16]. In MICCAI BraTS 2017 competition [17], most participants used U-Net variants, as the winner [18] simply assembled three kinds of the most common deep learning models, namely FCN (fully convolutional network) [19], V-Net [20], and DeepMedic [13]. Other than deep learning, some studies on brain cancer segmentation took advantage of fuzzy c-means clustering [21; 22; 23], cellular automata [24], random walker [8], and so on [5; 7; 25; 26]. However, they are not deep learning by not possessing over two hidden layers and will not be further discussed. However, few studies apply deep learning methods to the actual SRS datasets. Unlike the BraTS competitions, real applicable models may need to handle much more diversity rather than a single type of disease. [PERSON] et al. (2017) proposed a modification of DeepMedic that outperformed its parent method in segmentation during SRS treatment planning by adding a subpath, with a dice score reaching 0.67 in a cohort of 240 patients [27]. [PERSON] et al.