Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.

This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (IAC), leading to three major contributions; (1) The first publicly accessible PR image dataset with semantic annotations for 1,478 IACs and M3Ms from 1,010 patients is introduced, which includes challenging cases, such as false positive contacts, with CBCT images as the gold standard, (2) Established radiological indicators for M3M-IAC contact were extracted as features using digital image processing, and these features were used as inputs for various ML methods. Eligibility was assessed through statistical analysis and radiologists evaluations. Clinical feedback from radiologists on these features provides insights for future improvements.

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.

An Operational Burden Issue: Deciding on a Corneal Recipient From the Corneal Transplant Wait List for Elective Corneal Transplant.

The cornea is the outermost tissue of the eye and must be transparent to maintain good visual function. Diseases with loss of corneal transparency (ie, corneal blindness) account for 10% of blindness worldwide. The treatment of this condition is only possible with corneal transplant from corneal tissue obtained from deceased donors.

Diverse Task Classification from Activation Patterns of Functional Neuro-Images Using Feature Fusion Module.

One of the emerging fields in functional magnetic resonance imaging (fMRI) is the decoding of different stimulations. The underlying idea is to reveal the hidden representative signal patterns of various fMRI tasks for achieving high task-classification performance. Unfortunately, when multiple tasks are processed, performance remains limited due to several challenges, which are rarely addressed since the majority of the state-of-the-art studies cover a single neuronal activity task.

INFLUENCE OF GLYCEMIC CONTROL AND BODY COMPOSITION ON IRISIN LEVELS IN CHILDREN WITH TYPE 1 DIABETES MELLITUS.

Aim: We investigated the relationship between irisin concentrations and glycemic control, body composition and anthropometric measures in children with type 1 diabetes mellitus.

Methods: The study involved 40 subjects with T1DM prospectively. Glycemic control was evaluated.

Investigation of the relationship between serum sclerostin and dickkopf-1 protein levels with bone turnover in children and adolescents with type-1 diabetes mellitus.

Objectives: Diabetes mellitus (DM) is widely known to have a detrimental effect on bone health and is associated with increased fracture risk. Recently, the Wnt/beta-catenin signaling pathway and its inhibitors sclerostin and dickkopf-1 (Dkk-1) were found to be involved in the control of bone mass. The present study aimed to measure serum sclerostin and Dkk-1 protein levels in children and adolescents with type-1 DM and compare with other bone turnover markers and bone mineral density (BMD).

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.

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.

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.

[A case of mesenterial larval cestodiosis in a dog in Izmir].

The organs of a dog who died suddenly without showing any clinical signs at a dog nursing home and rehabilitation center located in Izmir were sent to İzmir/Bornova Veterinary Control Institute, in order to determine the cause of death. The samples from the internal organs of the dog were examined in the Department of Parasitology, and numerous cestodes larvae were seen on the mesenterium. These larvae were identifed as tetrathyridia the second stage larvae of Mesocestoides spp by parasitological examination.

[Parasites observed in a zebra in Izmir].

This work consists of a case report on a zebra presented to our institute for the determination of the cause of death. The animal was subjected to necropsy before it was presented to our institute. During examination, ascarids in the intestines and myasis agents in the stomach were observed.

Helminth parasites found in hedgehogs (Erinaceus concolor) from Turkey.

Hedgehog diseases are becoming important issues for veterinary surgeons due to growing interest in this animal species among pet owners and an increase in cases of rescued hedgehogs requiring veterinary care. A parasitological study was carried out on hedgehogs (Erinaceus concolor) in the Bursa province of Turkey, found dead mainly due to road casualties, to determine their helminth parasite burden. The detected helminths and their prevalences were as follows: Physaloptera clausa (72.

[Helminth parasites of white bream (Blicca bjoerkna L. 1758) from Kocadere stream, Bursa].

Aim of this study was to investigate the helminths and their monthly prevalence in 120 Blicca bjoerkna (white bream) in the Kocadere stream (Bursa province) from February 2005 to January 2006. As a result, 98.3% of B.

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.

Classification of blood regions in IVUS images using three dimensional brushlet expansions.

The presence of non-coherent blood speckle patterns makes the assessment of lumen size in intravascular ultrasound (IVUS) images a challenging problem, especially for images acquired with recent high frequency transducers. In this paper, we present a robust three-dimensional (3D) feature extraction algorithm based on the expansion of IVUS cross-sectional images and pullback directions onto an orthonormal complex brushlet basis. Several features are selected from the projections of low-frequency 3D brushlet coefficients.

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.