The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine.

Chronic dermatological ulcers cause great discomfort to patients, and while monitoring the size of wounds over time provides significant clues about the healing evolution and the clinical condition of patients, the lack of practical applications in existing studies impairs users' access to appropriate treatment and diagnosis methods. We propose the UTrack framework to help with the acquisition of photos, the segmentation and measurement of wounds, the storage of photos and symptoms, and the visualization of the evolution of ulcer healing. UTrack-App is a mobile app for the framework, which processes images taken by standard mobile device cameras without specialized equipment and stores all data locally.

Segmenting skin ulcers and measuring the wound area using deep convolutional networks.

Background And Objectives: Bedridden patients presenting chronic skin ulcers often need to be examined at home. Healthcare professionals follow the evolution of the patients' condition by regularly taking pictures of the wounds, as different aspects of the wound can indicate the healing stages of the ulcer, including depth, location, and size. The manual measurement of the wounds' size is often inaccurate, time-consuming, and can also cause discomfort to the patient.

dp-BREATH: Heat maps and probabilistic classification assisting the analysis of abnormal lung regions.

Background And Objective: Identifying abnormalities in chest CT scans is an important and challenging task, demanding time and effort from specialists. Different parts of a single lung image may present both normal and abnormal characteristics. Thus, detecting a single lung as healthy (normal) or not is inaccurate.

Employing Domain Indexes to Efficiently Query Medical Data From Multiple Repositories.

Content-based retrieval still remains one of the main problems with respect to controversies and challenges in digital healthcare over big data. To properly address this problem, there is a need for efficient computational techniques, especially in scenarios involving queries across multiple data repositories. In such scenarios, the common computational approach searches the repositories separately and combines the results into one final response, which slows down the process altogether.