End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

Purpose: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.

Materials And Methods: In this retrospective multicenter study, brain DWI studies from January 2017 to April 2020 from Center 1, from June 2020 to December 2020 from Center 2, and from November 2019 to April 2020 from Center 3 were included. Four radiologists labeled images into five classes: anterior cerebral artery (ACA), middle cerebral artery (MCA), posterior circulation (PC), and watershed (WS) regions, as well as normal images.

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a dedicated stroke center. A retrospective collection of DWI images of 271 and 122 consecutive acute ischemic stroke patients from two centers was carried out. Pretrained MobileNetV2 and EfficientNetB0 architectures were used to classify territorial subtypes as middle cerebral artery, posterior circulation, or watershed infarcts along with normal slices.

Artificial Intelligence in Stroke Imaging: A Comprehensive Review.

The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are important components of these chronic conditions. Stroke is the acute cessation of blood in the brain, which can lead to rapid tissue loss.

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

Purpose: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions.

Materials And Methods: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels.

Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID-19.

Several data sets have been collected and various artificial intelligence models have been developed for COVID-19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones.

Myofascial Trigger Point Identification in B-Mode Ultrasound: Texture Analysis Versus a Convolutional Neural Network Approach.

Objective: Myofascial pain syndrome (MPS) is one of the most common causes of chronic pain and affects a large portion of patients seen in specialty pain centers as well as primary care clinics. Diagnosis of MPS relies heavily on a clinician's ability to identify the presence of a myofascial trigger point (MTrP). Ultrasound can help, but requires the user to be experienced in ultrasound.

Measurement of the active toxoplasma retinochoroiditis lesion size during the disease course with swept-source optical coherence tomography angiography: A retrospective image analysis.

Purpose: To measure the lesion size reduction in eyes with active toxoplasma retinochoroiditis during the disease course with swept-source optical coherence tomography angiography (SS-OCTA).

Methods: We retrospectively analysed the chorioretinal lesion size in a group of 14 eyes with a single active toxoplasma retinochoroiditis lesion. SS-OCTA was performed at the baseline and follow-up in all eyes.

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks.

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance are hard to interpret.

Efficacy of Topical Brinzolamide Treatment in Posterior Microphthalmos-Related Macular Cystoid Lesions: A Case Series.

The aim of this study was to report the outcome of topical brinzolamide 1% treatment on macular cystoid lesions resembling retinoschisis in 4 patients diagnosed with posterior microphthalmia. The medical records of 4 patients with a clinical diagnosis of posterior microphthalmia who had started topical brinzolamide 1% treatment were reviewed. Visual acuity, central foveal thickness, and cystoid lesion area percentage were used to evaluate treatment response.

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage.

Background: DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

Purpose: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data.

Implementation and use of 3D pairwise geodesic distance fields for seeding abdominal aortic vessels.

Purpose: Precise extraction of aorta and the vessels departing from it (i.e. coeliac, renal, and iliac) is vital for correct positioning of a graft prior to abdominal aortic surgery.

Exploring Brushlet Based 3D Textures in Transfer Function Specification for Direct Volume Rendering of Abdominal Organs.

Intuitive and differentiating domains for transfer function (TF) specification for direct volume rendering is an important research area for producing informative and useful 3D images. One of the emerging branches of this research is the texture based transfer functions. Although several studies in two, three, and four dimensional image processing show the importance of using texture information, these studies generally focus on segmentation.

Performance comparison of compression algorithms for archiving segmented volumetric binary medical data.

Archiving result of a segmentation task allows the representation of the segmented volume at a later time. The segmented volume can be stored in a binary format, which can be restored by a simple combination of the original data with this binary information. Since, the sizes of the segmented binary data have high memory requirements; a lossless compression method should be employed for efficient archiving.

Semi-automatic segmentation methods for 3-D visualization and analysis of the liver.

Pre-evaluation of donors prior to surgery of living donated liver transplantation is one of the challenging applications that computer aided systems are needed. The precise measurement of liver volume requires effective segmentation procedures, while three dimensional rendering of the segmented data provides demonstrative information to radiologists and surgeons before surgery. The Insight Toolkit provides effective algorithms for segmentation, which are also optimized for high computational performance and processing time.

Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy.

Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision.

Integrating segmentation methods from different tools into a visualization program using an object-based plug-in interface.

In medical visualization, segmentation is an important step prior to rendering. However, it is also a difficult procedure because of the restrictions imposed by variations in image characteristics, human anatomy, and pathology. Moreover, what is interesting from clinical point of view is usually not only an organ or a tissue itself, but also its properties together with adjacent organs or related vessel systems that are going in and coming out.

Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks.

As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs.

Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation.

Identifying liver region from abdominal computed tomography-angiography (CTA) data sets is one of the essential steps in evaluation of transplantation donors prior to the hepatic surgery. However, due to gray level similarity of adjacent organs, injection of contrast media and partial volume effects; robust segmentation of the liver is a very difficult task. Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder.

A software tool for interactive generation, representation, and systematical storage of transfer functions for 3D medical images.

As being a tool that assigns optical parameters, i.e. color, transparency, used in interactive visualization, transfer functions have very important effects on the quality of volume rendered medical images.