**4.6 Neuro-oncology**

For over a century, neurosurgeons have played an essential role in the management of cancers afflicting the central nervous system (CNS). As the tenth leading cause of death for both men and women, accurate clinical evaluation of disease progression, and early detection of brain tumors using effective brain imaging techniques is paramount to improving patient outcomes. Historically, the preoperative phase involved manual segmentation of brain tumors and small related brain structures by the neurosurgeon – a laborious task [87]. Hence, many automated solutions have been explored, with the broadest categories for automated brain tumor segmentation of MR images including (i) intensity-based, (ii) ML-based, and (iii) hybrid-based approaches.

The intensity-based approaches are among the most conventional methods used in brain tumor segmentation, relying on a basic analysis of pixel values within the spatial domain. The thresholding technique, for instance, functions by binarizing the MR image by pixel intensity relative to an intensity threshold [87]. This technique, however, suffers from many limitations including sensitivity to noise and intensity non-homogeneity. Also classified as an intensity-based approach, the region-based method involves using pre-defined pixel/voxel conditions to extract intensity information by locating a region following seed point selection and connecting pixels with similar intensity values; many studies have recently improved upon this technique but suffer from limitations such as inability to remove noise, subjective manual setting of parameters, and annotation bias [88–92]. Most existing methods rely on such fully supervised methods [93].

Largely due to the aforementioned constraints and inflexibility, ML-based approaches to brain tumor segmentation have increasingly been explored, both in traditional ML as well as DL forms. Many recent studies leveraging traditional ML models have shown equal or superior performance relative to the conventional intensity-based models, though observing limitations in some studies such as subjective user-directed pixel label refinement of segmentation results, sensitivity to noise and distortions, non-uniform intensity distribution, and extraction of redundant features [94–96].

In the past decade, interest toward deep learning as applied to brain tumor segmentation has soared in popularity due to its anticipated superior performance compared to more conventional models of data abstraction. Many studies have relied on extracting 2D patches from 3D MR images to use as inputs for the 2D CNN [97–109]. Though CNNs have generally demonstrated improved performance compared to its

intensity-approach counterparts, model training is often time-consuming as a large amount of training data, parameters, and processing power are required. Furthermore, 3D contextual information is often bypassed in 2D CNNs, thus spurring the development of 3D CNNs in recent years [110–115]. Although 3D CNNs enable better exploitation of 3D features from MR image information data, high computational resources (i.e. high network intensiveness and memory consumption) limit its widespread applicability. Thus, 2.5D deep neural networks (DNNs) approaches have been explored; Wang et al. validated a cascaded 2.5D model which improved segmentation accuracy by striking a balance between memory consumption and model complexity, demonstrating superior inference compared to already established models such as DeepMedic and ScaleNet [116–118].

Recently, Pham et al. introduced a hybrid metaheuristic-ML model to circumvent sensitivity to noise, intensity non-uniformity, and trapping into local minima and dependency on initial clustering centroids [119]. However, this model suffered from decreases in performance, though its introduction spurred the development of many hybrid models to find an optimal balance between each efficiency metric [96, 119–122]. Other hybrid approaches such as DL-traditional ML and ML-contour based models, though better than conventional methods, have not observed overall efficiency greater than the metaheuristic-ML hybrid [87]. At the present moment, the literature indicates that deep learning based and hybrid-based metaheuristic models are the most efficient and reliable methods available, though its widespread application requires further validation. Despite improvements in deep learning models as applied to brain tumor segmentation, it is imperative to note that limitations in tumor morphological uncertainty, low contrast resolution, annotation biases during data labeling, and imbalanced voxel distribution persist. Thus, advances in AI can aid the neurosurgeon in various brain tumor segmentation contexts though neurosurgeons should remain cautious when using DL models to inform his or her clinical judgment.
