### 1.1 Image Processing and Analysis for Preclinical and Clinical Applications
#### 1.1.8 Principal and Clinical Applications  
1  
1 Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefala, Italy 1  
2 Department of Radiology, University Hospital of Padova, Via Nicolo Gi system detects four diameter endpoints from the transverse images and two diameter endpoints from the sagittal images, as defined by the classical procedure. These endpoints are estimated using a new image-patch voting method to address characteristic problems of ultrasound images. Furthermore, the dataset included 75 MRI images of the initial 305 patients. The results showed optimal performance, confirming that this system can be used in clinical practice. Another prostate gland segmentation method based on T2-weighted MRI was proposed by [PERSON] et al. [7]. The authors presented the efficient neural network (ENet) to tackle the fully automated, real-time, and 3D delineation process of the prostate. ENet is mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. The authors applied this network to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function [11] and compared the results with UNet and ERFNet (efficient residual factorized convNet). The results showed that ENet and UNet were more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where the graphics processing unit (GPU) is not always available. In a similar study, [PERSON] et al. [8] used ENet and ERFNet for the automatic segmentation of retroperitoneal sarcoma (RPS) in 94 CT examinations. The volume estimation of RPS is often difficult due to its huge dimensions and irregular shape; thus, it often requires manual segmentation, which is time-consuming and operator-dependent. For this reason, the authors assessed the existence of significant differences between manual segmentation performed by two radiologists and automatic segmentation based on ENet and ERFNet using analysis of variance (ANOVA). A set of performance indicators for the shape comparison were calculated, namely sensitivity, positive predictive value, dice similarity coefficient, volume overlap error, and volumetric differences. There were no significant differences found between the RPS volumes obtained using manual segmentation and deep learning methods. Furthermore, all performance indicators were optimal for both ENet and ERFNet. Finally, ENet took around 15 s for segmentation versus 13 s for ERFNet by using GPU. In the case of CPU, ENet took around 2 min versus 1 min for ERFNet. The manual approach required approximately one hour per segmentation. In conclusion, fully automatic deep learning networks were reliable methods for RPS volume assessment. [PERSON] et al. [9] proposed a tool for the detection of basal cell carcinoma (BCC) to provide a prioritization in the tele-dematology consultation. BCC is the most frequent skin cancer, and its increasing incidence is producing a high overload in dermatology services. The authors analyzed if pre-segmentation of the lesion improved the classification of the lesion. After that, they analyzed three deep neural networks to distinguish between BCC and nevus, or other skin lesions. The best segmentation results were obtained with SegNet with accuracies of 98% and 95% for distinguishing BCC from nevus and other skin lesions, respectively. This method outperformed the winner of the challenge International Skin Imaging Collaboration (ISIC) 2019. Furthermore, the authors concluded that when deep neural networks are used to classify, a pre-segmentation of the lesion does not improve the classification results. Finally, a novel maxillofacial fracture detection system (MFDS), based on convolutional neural networks and transfer learning, was proposed by [PERSON] et al.