4 research outputs found
Volumetric medical image segmentation with deep learning pipelines
Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the level of success that they have achieved. In this work, we first leverage recent architectural innovations to make an initial segmentation: (i) spatial and channel-wise squeeze and excitation mechanism; (ii) a 3D U-Net++ network and deep supervision. Second, we use classical methods for refining the initial segmentation: (i) spatial normalization and (ii) local 3D refinement network applied to patches. Finally, we put our methods together in a novel segmentation pipeline. We train and evaluate our models and pipelines on a dataset of a 120 abdominal magnetic resonance - volumetric - images (MRIs). The goal is to segment five different organs of interest (ORI): liver, kidneys, stomach, duodenum, and large bowel. Our experiments show that we can generate full resolution segmentation of comparable quality to the state-of-the-art methods without adding computational cost.Includes bibliographical references
The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review
Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes.
Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing.
Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models.
Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision.
Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data
The Cell Tracking Challenge: 10 years of objective benchmarking
The Cell Tracking Challenge is an ongoing benchmarking initiative that
has become a reference in cell segmentation and tracking algorithm
development. Here, we present a signifcant number of improvements
introduced in the challenge since our 2017 report. These include the
creation of a new segmentation-only benchmark, the enrichment of
the dataset repository with new datasets that increase its diversity and
complexity, and the creation of a silver standard reference corpus based
on the most competitive results, which will be of particular interest for
data-hungry deep learning-based strategies. Furthermore, we present
the up-to-date cell segmentation and tracking leaderboards, an in-depth
analysis of the relationship between the performance of the state-of-the-art
methods and the properties of the datasets and annotations, and two
novel, insightful studies about the generalizability and the reusability
of top-performing methods. These studies provide critical practical
conclusions for both developers and users of traditional and machine
learning-based cell segmentation and tracking algorithms.Web of Science2071020101