# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.3 Image Segmentation  
AI-based methods have shown potential for medical image segmentation, target detection, and other tasks [24; 25; 26; 27] using manual segmentation performed by expert radiation oncologists as the ground truth.  
Image segmentation is a process of the RT workflow that shows substantial intra- and inter-observer variability [28]. It is crucial for treatment plan development because RT plans are optimized and judged based on contoured regions and the fulfillment of dose-volume constraints. Therefore, the absorbed dose distribution metrics depend on the accuracy and integrity of contours used to identify target and normal tissues. The possibility of using AI-based segmentation might reduce the inter-observer variability in the delineation of OARs and targets.  
Nevertheless, manual segmentation is a time-consuming process, which impacts the work schedule of the RT department. The time required for this task is relevant not only for baseline computed tomography (CT) planning images but also for offline or online adaptive RT, requiring repeated CT images or daily acquired cone-beam CT (CBCT). Indeed, the time delay between image acquisition and manual segmentation can last several minutes, which might be incompatible with the online adaptive process, because in the meantime the targets and OARs might change in volume and position [29]. ML and DL-based methods were applied for automatic image segmentation in 15 and 254 identified papers, respectively, to mimic the expert radiation oncologists' results in the delineation and identification of organs and tumors (Figure 4).  
AI-based segmentation tools have been applied to different anatomical areas (Figure 5a), such as the brain [30; 31], head and neck [25; 26; 27; 27; 28; 29; 32; 33; 34; 35; 36; 37; 38; 39], thorax (including the segmentation of lungs [40; 41; 42; 43; 44], breasts [45; 46; 47; 44], and heart [44; 47]), abdomen [24; 25; 48], and female or male pelvis [49; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54]. The use of AI for automatic segmentation might generate contours of lesions and OARs with an expected higher adherence to international guidelines [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65], reducing the inter-operator variability, especially in a multicentric context.  
Several commercial, open-source research tools are now available for auto-segmentation [32; 33; 34; 35; 36; 39; 41; 47; 66; 67; 68; 69]. Among the investigated papers, 138 used a convolutional neural network (CNN) specifically designed for image recognition and computer vision applications. These techniques were applied in the RT field to segment lesions or OARs in several areas and image modalities. Excluding CT alone, the second most frequent imaging modality is MR, thanks to its ability to provide superior soft-tissue contrast and PET/CT images to obtain metabolic information (Figure 5b).  
Ongoing significant efforts are still directed towards improving the efficiency and robustness of automatic delineation and assessment strategies for QA with AI-based segmentation tools [42; 70; 71; 72], requiring ad hoc guidelines [45]. In particular, [PERSON] et al. [70] developed an ML approach based on the use of a radiomic-feature-based classifier to evaluate the segmentation quality of the heart's structure. Moreover, [PERSON] et al. [71] used spatial probability maps to detect inaccuracies in contour delineation of the head and neck using a DL approach. Finally, DL-based methods were used to evaluate the quality of contouring of OARs and targets in lung cancer patients [42] and salivary glands in head and neck cancer patients [72].