Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI.

Purpose: To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images.

Materials And Methods: In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn the normal variability of the brain from MR scans of healthy individuals. A total of 100 normal, in-house MR scans were used for training.

Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study.

Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples.

GANs for medical image analysis.

Generative adversarial networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed, and potential future work is elaborated.

Robust navigation support in lowest dose image setting.

Purpose: Clinical cardiac electrophysiology (EP) is concerned with diagnosis and treatment of cardiac arrhythmia describing abnormality or perturbation in the normal activation sequence of the myocardium. With the recent introduction of lowest dose X-ray imaging protocol for EP procedures, interventional image enhancement has gained crucial importance for the well-being of patients as well as medical staff.

Methods: In this paper, we introduce a novel method to detect and track different EP catheter electrodes in lowest dose fluoroscopic sequences based on [Formula: see text]-sparse coding and online robust PCA (ORPCA).

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users.

Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences.

Purpose: Catheter guidance is a vital task for the success of electrophysiology interventions. It is usually provided through fluoroscopic images that are taken intra-operatively. The cardiologists, who are typically equipped with C-arm systems, scan the patient from multiple views rotating the fluoroscope around one of its axes.

Detection of proton release from cultured human myotubes to identify malignant hyperthermia susceptibility.

Background: Malignant hyperthermia (MH) is a pharmacogenetic disorder of skeletal muscle. During general anesthesia, a life-threatening hypermetabolic state may occur resulting from increased release of Ca2+ from the sarcoplasmic reticulum in skeletal muscle. Diagnosis of MH susceptibility requires surgical muscle biopsies to measure force in response to chemical stimulation (in vitro contracture test, IVCT).