Fast and accurate 3-D spine MRI segmentation using FastCleverSeg.

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy.

Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges.

Objectives: Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health.

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm.

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems.

LargeNetVis: Visual Exploration of Large Temporal Networks Based on Community Taxonomies.

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible.

ClinicalPath: A Visualization Tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making.

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data.

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.

A superpixel-driven deep learning approach for the analysis of dermatological wounds.

Background: The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers.

Method: QTDU consists of a three-stage pipeline for the obtaining of ulcer segmentation, tissues' labeling, and wounded area quantification.

Transforming Two Decades of ePR Data to OMOP CDM for Clinical Research.

This paper presents the extract-transform-and-load (ETL) process from the Electronic Patient Records (ePR) at the Heart Institute (InCor) to the OMOP Common Data Model (CDM) format. We describe the initial database characterization, relational source mappings, selection filters, data transformations and patient de-identification using the open-source OHDSI tools and SQL scripts. We evaluate the resulting InCor-CDM database by recreating the same patient cohort from a previous reference study (over the original data source) and comparing the cohorts' descriptive statistics and inclusion reports.

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.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles.

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity.

Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI.

In this paper we address the "skull-stripping" problem in 3D MR images. We propose a new method that employs an efficient and unique histogram analysis. A fundamental component of this analysis is an algorithm for partitioning a histogram based on the position of the maximum deviation from a Gaussian fit.

Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses.

This paper presents the use of relevance feedback (RFb) to reduce the semantic gap in content-based image retrieval (CBIR) of mammographic masses. Tests were conducted where the radiologists' classification of the lesions based on the BI-RADS categories were used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon Zernike moments.

A new family of distance functions for perceptual similarity retrieval of medical images.

A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes.

Using an image-extended relational database to support content-based image retrieval in a PACS.

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database.