Advances in Signal and Image Processing for Biomedical Applications

Dear Colleagues,

This Special Issue aims to highlight recent advances in signal and image processing techniques in medical applications. Over the past decade, deep learning techniques have revolutionized the processing of signals, images, and videos. Deep-learning-based algorithms have achieved great success, especially in natural language processing and tasks related to image and vision, such as classification, recognition, detection, segmentation, and reconstruction. The progress in deep learning technology is largely driven by the improvement of computing power (especially parallel computing power) and the convenience of building large-scale training data sets with the Internet. However, in the field of medical applications, deep learning still faces many challenges, mainly including: 1) the cost of medical data collection and labeling is high; 2) the data volume of medical data (e.g., high-resolution CT) is large, which usually requires more efficient processing algorithms; and 3) the black-box nature of deep learning methods leads to their lack of interpretability in medical tasks, and it is difficult to gain the trust of doctors, regulators and patients.

This Special Issue welcomes all recent research works in signal and image processing for applications in medicine, especially those that fuse traditional and deep learning techniques, unsupervised or self-supervised methods, and interpretable deep learning models built for medical purposes. Potential topics in this collection include, but are not limited to, the following topics:

• Medical image (e.g., CT, MRI, ultrasound) processing
• Surgical vision (applications of computer vision in surgery)
• Medical signal processing
• Medical image reconstruction
• Medical image classification, detection, localization and segmentation
• Intelligent diagnostic system based on medical signals, image and data

Dr. Bo Yang
Dr. Bo Jin
Prof. Dr. Xiaoyan Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Title: THE FAILURE OF BIOMARKERS TO PREDICT THE HIGH ALLERGIC RISK NEONATE AND INFANT : THE CAPE TOWN DATA
Authors: MATTHIAS HAUS
Affiliation: Department of Paediatrics, University of Pretoria, South Africa

Title: 3D color multimodality fusion imaging as an educational and surgical planning tool for extracerebral tumors
Authors: Xiaolin Hou; Ru xiang Xu; Dongdong Yang; Dingjun Li
Affiliation: Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital； Hospital of Chengdu University of Traditional Chinese Medicine
Abstract: BACKGROUND Extracerebral tumors often occur on the surface of the brain or at the skull base. It is particularly important to identify the peritumoral sulci, gyri, and nerve fibers. Preoperative visualization of three-dimensional (3D) multimodal fusion imaging (MFI) is crucial for extracerebral tumor surgery. However, the traditional 3D-MFI brain models are homochromatic and do not allow easy identification of anatomical functional areas.METHODS In this study, 33 patients with extracerebral tumors without peritumoral edema were retrospectively recruited. They underwent 3D T1-weighted MRI, Diffusion tensor imaging (DTI), and CT angiography (CTA) sequence scans. 3DSlicer, Freesurfer, and BrainSuite were used to explore 3D-color-MFI and preoperative planning for those patients. To determine the effectiveness of 3D-color-MFI as a teaching tool for neurosurgeons as well as a patient education and communication tool, questionnaires were administered to 15 neurosurgery residents and all patients, respectively. RESULTS For neurosurgical residents, 3D-color-MFI enabled a better comprehension of surgical anatomy and more efficient techniques for removing extracerebral tumors than traditional 3D-MFI (P<0.001). For patients, utilizing 3D-color-MFI can significantly improve their understanding of the surgical approach and risks (P<0.005). CONCLUSIONS 3D-color-MFI is more helpful for learning surgical anatomy, developing surgical strategies, and improving communication with patients than traditional 3D-MFI. 3D-color-MFI is a tool with great promise for extracerebral tumors.