This algorithm, which was applied to medical tooth specimen images, demonstrated good performances, achieving better noise reduction in comparison with the conventional methods, as well as good edge and other fine-detail preservation. A method to decode the electroencephalography (EEG) signals, evoked when individuals perceive different types of visual motion stimuli, is described [19]. This method, which was based on attention mechanisms and a variant of Recurrent Neural Networks (RNNs), demonstrated potential application in the brain-computer interface (BCI) system based on visual motion perception. A new method of automatic segmentation of macular edema regions in retinal OCT images is proposed [20]. It is based on an improved version of UNet (U-Net++), which exploits the ResNet architecture as the backbone, with re-designed skip pathways and a dense convolution block. The experimental results demonstratedexcellent segmentation results, leading to more accurate regions and correctly detecting diverse edema in multiple regions. An automated frame selection and stitching framework, useful for classifying eardrum abnormalities, is illustrated in [21]. It is able to increase the probability of detecting a lesion, even if it appears in a few frames of the otoscope videos, by using image segmentation techniques to create enhanced composite images. A universally affordable open-source Android application used to estimate the knee instability is proposed [22]. Such instability caused by a deficiency of the anterior cruciate ligament (ACL) could be estimated by assessing the translation of the tibia with respect to the femur. The experimental results demonstrated that the application could help assess knee instability quantitatively and accurately. An approach for automatic prostate segmentation in MRI scans, based on a cascaded dual attention network (CDA-Net), is proposed [23]. The algorithm achieved very good segmentation performance in a variety of complex slice images and surpassed the state-of-the-art algorithms in different benchmark datasets. It could be very useful for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. A denoising algorithm for noise reduction from digital images able to preserve the edge information of objects better than conventional denoising filters, as also demonstrated by the experimental results on thoracic CT images, is illustrated [24]. A reliable image registration protocol is crucial in multimodal longitudinal skeletal muscle Magnetic Resonance Imaging (MRI) studies in order to extract reliable parameters that can be used as indicators for physio/pathological characterization of muscle tissue and assess the effectiveness of treatments [25]. Extensive cross-dataset experimentation on several CNN-based state-of-the-art cell nuclei segmentation methods has been performed in order to assess their performances in real-world, challenging application scenarios. The results show that some of the existing CNN-based approaches are capable of generalizing to target images that resemble those used for training. In contrast, their effectiveness considerably degrades when the target and source significantly differ in color and scale [26]. A tool for accurate determination of cell confluence, which is critical for generating reasonable results in cell biological studies, is proposed. The experimental results, performed on cell images of human normal oral fibroblasts (hOFs) acquired under an inverted microscope, demonstrated that this tool could speed up the analysis and prevent unnecessary human-made mistakes [27]. The articles published in this Special Issue are related to biomedical fields and applications, and are all different from each other. Some of them are older and already addressed in the literature, while others are more recent, for which there are still many open issues and much room for improvement.