3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities

Accurate, automated quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided diagnosis (CADx) systems to support the in- terpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, varying image spatial resolutions resulting from different scanner protocols, and the presence of blurring artefacts. This paper presents a novel computing ap- proach for automated organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learning techniques in a two-part process. (1) a 3D encoder-decoder, Rb-UNet, builds a localisation model and a 3D Tiramisu network generates a boundary-preserving segmentation model for each target structure; (2) the fully trained Rb-UNet predicts a 3D bounding box encapsulating the target structure of interest, after which the fully trained Tiramisu model performs segmentation to reveal organ or muscle boundaries for every protrusion and indentation. The proposed approach is evaluated on six different datasets, including MRI, Dynamic Contrast Enhanced (DCE) MRI and CT scans targeting the pancreas, liver, kidneys and iliopsoas muscles. We achieve quantitative measures of mean Dice similarity coefficient (DSC) that surpasses or are comparable with the state-of-the-art and demonstrate statistical stability. A qualitative evaluation performed by two independent experts in radiology and radiography verified the preservation of detailed organ and muscle boundaries

An investigation on Turkish primary school students' perceptions about global warming and their thoughts on preventing global warming

This research aims to determine the perceptions of primary level students' about global warming and their thoughts about stopping global warming. This research used one form of qualitative research design, the case study method. This case study is carried out in two different socioeconomic environments in Turkey's Kirsehir province with students from grades of 4th to 8th. A Primary School was selected to represent the higher socioeconomic environment and B Primary School was selected to represent the lower socioeconomic environment. The research group was composed of 40 students, 20 students from each school. The semi-structured interview method was used to collect the data. The results showed that the vast majority of students have insufficient knowledge or misconception about global warming. Additionally the primary students have produced very interesting solutions in order to prevent global warming. © IDOSI Publications, 2013

Head motion measurement and correction using FID navigators.

To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array. FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms. FIDnav motion estimates achieved mean absolute errors of 0.34 ± 0.49 mm and 0.52 ± 0.61° across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion. Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations

Prospective motion correction in kidney MRI using FID navigators.

Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time. FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique. Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method. The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice

Preservice science teachers' belief systems about teaching a socioscientific issue

We investigated the belief system of Turkish preservice science teachers (PSTs) about teaching a socioscientific issue (GM Foods) using a belief system model. This model includes three belief pools: content beliefs (CBs), core pedagogical beliefs (CPBs) and pedagogy of content beliefs (PCBs). Based on this model, we developed a questionnaire in order to see interrelationships among three belief pools about teaching GM Foods. For content beliefs, we selected content knowledge, risk perceptions, moral beliefs and religious beliefs. For pedagogy of content beliefs, we selected teaching efficacy, preferred teaching methods and preferred teacher's roles. We administered the questionnaire to 423 PSTs. Using correlation analysis, multinomical logistic regression and structural equation modelling we tried to understand the relationships between CBs and PCBs and to make interpertations about possible CPBs working as a filter between CBs and PCBs. The results show that PSTs are relatively knowledgeable, hold high risk perceptions and certain moral and religious beliefs about GM Foods. They possess high teaching efficacy beliefs, choose the teaching role of Neutral Impartiality and prefer large class discussion and computer-assisted teaching. As core pedagogical beliefs (CPBs), they may have traditional epistemologies, moral and religiously-based teaching goals. © ISSN:1304-6020

External hardware and sensors, for improved MRI

Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. “Classic” examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily “mechanical” (eg acceleration, speed, and torque), “acoustic” (sound and ultrasound), “optical” (light and infrared), or “electromagnetic” in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities

Austauschbarkeit von Ressourcen Schlussbericht

SIGLEAvailable from TIB Hannover: FR 5307 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman