800 research outputs found
Data for paper 'Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor'
No full textPaper and statistic data for journal:
Ruobing Huang, Ana Namburete, Alison Noble, "Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor," J. Med. Imag. 5(1), 014007 (2018), doi: 10.1117/1.JMI.5.1.014007.
Future research can therefore compare relative results
Repositioning the graphic designer as researcher
No full textIn academic terms, the discipline of graphic design is relatively young. Consequently the position of the discipline within academic territory, and the role of the designer, continue to be debated. In part, these debates have been a product of attempts to define and defend the discipline’s borders from within, in order to establish a sense of the role of graphic design and the graphic designer as commensurate with other disciplines both within and beyond art and design. In recent years graphic designers have variously been defined as ‘authors’, ‘producers’ and ‘readers’, yet none of these definitions seem to have provided any kind of productive or lasting impact within the academy. This paper suggests that rather than continue to seek territorial definitions and positions from within, it could be more productive to look beyond the confines of the discipline. Gaining a broader, interdisciplinary perspective on, and understanding of, qualitative research methods from other disciplines may enable the graphic designer to more fully position his or her practice within the wider academy. Such a perspective could help facilitate the repositioning and redefinition of the graphic designer as ‘researcher’ - a move that would be productive in relation to the future development of postgraduate research within the discipline
Differentiating operator skill during routine fetal ultrasound scanning using probe motion tracking
No full textIn this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators’ personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95% accuracy
Quantifying regional left ventricular function using spatio-temporal tracking techniques
No full textIncreasingly, diagnosis of cardiac disease, relies on computer processing of images to aid decision making. In this thesis, we use echocardiography, which is the most widely used cardiac imaging modality to study the motion of the left ventricle. Currently, clinical reporting of echocardiography examinations is operator-dependent and largely qualitative. Commercially available software does not track the left ven- tricle. Also, it does not provide quantification of regional function. This thesis establishes a framework for the quantitative regional analysis of left ven- tricular function. The endocardial and epicardial contours are automatically tracked during the cardiac cycle. A quantitative measure of regional endocardial wall excur- sion and myocardial thickening, based on a 16-segment model of the heart, is then obtained based on these boundaries. The new tracking framework is based on Kalman filtering which makes a single pre- diction as to the position of the boundary on the next frame. We develop a mea- surement model for the endocardial border, the tissue/blood interface, and the epi- cardium, the tissue/tissue interface. Having tracked the endocardial and epicardial boundaries, we introduce an interpretational space which provides clinically mean- ingful regional quantitative measures of left ventricular function. We illustrate all the concepts on one example. We apply the ideas developed to stress echocardiography, in a small retrospective clinical test
Going deeper into cardiac motion analysis to model fine spatio-temporal features
No full textThis paper shows that deep modelling of subtle changes of cardiac motion can help in automated diagnosis of early onset of cardiac disease. In this paper, we model left ventricular (LV) cardiac motion in MRI sequences, based on a hybrid spatio-temporal network. Temporal data over long time periods is used as inputs to the model and delivers a dense displacement field (DDF) for regional analysis of LV function. A segmentation mask of the end-diastole (ED) frame is deformed by the predicted DDF from which regional analysis of LV function endocardial radius, thickness, circumferential strain (Ecc) and radial strain (Err) are estimated. Cardiac motion is estimated over MR cine loops. We compare the proposed technique to two other deep learning-based approaches and show that the proposed approach achieves promising predicted DDFs. Predicted DDFs are estimated on imaging data from healthy volunteers and patients with primary pulmonary hypertension from the UK Biobank. Experiments demonstrate that the proposed methods perform well in obtaining estimates of endocardial radii as cardiac motion-characteristic features for regional LV analysis
Ultrasound Video Segmentation with Adaptive Temporal Memory
No full textAutomated segmentation of anatomical structures in fetal ultrasound video is challenging due to the highly diverse appearance of anatomies and image quality. In this paper, we propose an ultrasound video anatomy segmentation approach to iteratively memorise and segment incoming video frames, which is suitable for online segmentation. This is achieved by a spatio-temporal model that utilizes an adaptive memory bank to store the segmentation history of preceding frames to assist the current frame segmentation. The memory is updated adaptively using a skip gate mechanism based on segmentation confidence, preserving only high-confidence predictions for future use. We evaluate our approach and related state-of-the-art methods on a clinical dataset. The experimental results demonstrate that our method achieves superior performance with an F1 score of 84.83%. Visually, the use of adaptive temporal memory also aids in reducing error accumulation during video segmentation
Model-based segmentation methods for analysis of 2d and 3d ultrasound images and sequences
No full textThis thesis describes extensions to 2D and 3D model-based segmentation algorithms for the analysis of ultrasound images and sequences. Starting from a common 2D+t "track-to-last" algorithm, it is shown that the typical method of searching for boundary candidates perpendicular to the model contour is unnecessary if, for each boundary candidate, its corresponding position on the model contour is optimised jointly with the model contour geometry. With this observation, two 2D+t segmentation algorithms, which accurately recover boundary displacements and are capable of segmenting arbitrarily long sequences, are formulated and validated. Generalising to 3D, subdivision surfaces are shown to be natural choices for continuous model surfaces, and the algorithms necessary for joint optimisation of the correspondences and model surface geometry are described. Three applications of 3D model-based segmentation for ultrasound image analysis are subsequently presented and assessed: skull segmentation for fetal brain image analysis; face segmentation for shape analysis, and single-frame left ventricle (LV) segmentation from echocardiography images for volume measurement. A framework to perform model-based segmentation of multiple 3D sequences - while jointly optimising an underlying linear basis shape model - is subsequently presented for the challenging application of right ventricle (RV) segmentation from 3D+t echocardiography sequences. Finally, an algorithm to automatically select boundary candidates independent of a model surface estimate is described and presented for the task of LV segmentation. Although motivated by challenges in ultrasound image analysis, the conceptual contributions of this thesis are general and applicable to model-based segmentation problems in many domains. Moreover, the components are modular, enabling straightforward construction of application-specific formulations for new clinical problems as they arise in the future
Analysis of 3D echocardiography
No full textHeart disease is the major cause of death in the developed world. Due to its fast, portable, low-cost and harmless way of imaging the heart, echocardiography has become the most frequent tool for diagnosis of cardiac function in clinical routine. However, visual assessment of heart function from echocardiography is challenging, highly operatordependant and is subject to intra- and inter observer errors. Therefore, development of automated methods for echocardiography analysis is important towards accurate assessment of cardiac function. In this thesis we develop new ways to model echocardiography data using Bayesian machine learning methods and concern three problems: (i) wall motion analysis in 2D stress echocardiography, (ii) segmentation of the myocardium in 3D echocardiography, and (iii) standard views extraction from 3D echocardiography. Firstly, we propose and compare four discriminative methods for feature extraction and wall motion classification of 2D stress echocardiography (images of the heart taken at rest and after exercise or pharmalogical stress). The four methods are based on (i) Support Vector Machines, (ii) Relevance Vector Machines, (iii) Lasso algorithm and Regularised Least Squares, (iv) Elastic Net regularisation and Regularised Least Squares. Although all the methods are shown to have superior performance to the state-of-the-art, one conclusion is that good segmentation of the myocardium in echocardiography is key for accurate assessment of cardiac wall motion. We investigate the application of one of the most promising current machine learning techniques, called Decision Random Forests, to segment the myocardium from 3D echocardiograms. We demonstrate that more reliable and ultrasound specific descriptors are needed in order to achieve the best results. Specifically, we introduce two sets of new features to improve the segmentation results: (i) LoCo and GloCo features with a local and a global shape constraint on coupled endoand epicardial boundaries, and (ii) FA features, which use the Feature Asymmetry measure to highlight step-like edges in echocardiographic images. We also reinforce the traditional features such as Haar and Rectangular features by aligning 3D echocardiograms. For that we develop a new registration technique, which is based on aligning centre lines of the left ventricles. We show that with alignment performance is boosted by approximately 15%. Finally, a novel approach to detect planes in 3D images using regression voting is proposed. To the best of our knowledge we are the first to use a one-step regression approach for the task of plane detection in 3D images. We investigate the application to standard views extraction from 3D echocardiography to facilitate efficient clinical inspection of cardiac abnormalities and diseases. We further develop a new method, called the Class- Specific Regression Forest, where class label information is incorporating into the training phase to reinforce the learning from semantically relevant to the problem classes. During testing the votes from irrelevant classes are excluded from voting to maximise the confidence of output predictors. We demonstrate that the Class-Specific Regression Random Forest outperforms the classic Regression Random Forest and produces results comparable to the manual annotations
Delving deep into fetal neurosonography: an image analysis approach
No full textUltrasound screening has been used for decades as the main modality to examine fetal brain development and to diagnose possible anomalies. However, basic clinical ultrasound examination of the fetal head is limited to axial planes of the brain and linear measurements which may have restrained its potential and efficacy. The recent introduction of three-dimensional (3D) ultrasound provides the opportunity to navigate to different anatomical planes and to evaluate structures in 3D within the developing brain. Regardless of acquisition methods, interpreting 2D/3D ultrasound fetal brain images require considerable skill and time.
In this thesis, a series of automatic image analysis algorithms are proposed that exploit the rich sonographic patterns captured by the scans and help to simplify clinical examination. The original contributions include:
1. An original skull detection method for 3D ultrasound images, which achieves mean accuracy of 2.2 ± 1.6 mm compared to the ground truth (GT). In addition, the algorithm is utilised for accurate automated measurement of essential biometry in standard examinations: biparietal diameter (mean accuracy: 2.1 ±1.4 mm) and head circumference (mean accuracy: 4.5 ± 3.7 mm).
2. A plane detection algorithm. It automatically extracts mid-sagittal plane that provides visualization of midline structures, which are crucial to assess central nervous system malformations. The automated planes are in accordance with manual ones (within 3.0±3.5°).
3. A general segmentation framework for delineating fetal brain structures in 2D images. The automatically generated predictions are found to be agreed with the manual delineations (mean dice-similarity coefficient: 0.79±0.07). As a by-product, the algorithm generated automated biometry. The results might be further utilized for morphological evaluation in future research.
4. An efficient localization model that is able to pinpoint the 3D locations of five key brain structures that are examined in a routine clinical examination. The predictions correlate with the ground truth: the average centre deviation is 1.8 ± 1.4 mm, and the size difference between them is 1.9 ± 1.5 mm. The application of this model may greatly reduce the time required for routine examination in clinical practice.
5. A 3D affine registration pipeline. Leveraging the power of convolutional neural networks, the model takes raw 3D brain images as input and geometrically transforms fetal brains into a unified coordinate system (proposed as a Fetal Brain Talairach system).
The integration of these algorithms into computer-assisted analysis tools may greatly reduce the time and effort to evaluate 3D fetal neurosonography for clinicians. Furthermore, they will assist understanding of fetal brain maturation by distilling 2D/3D information directly from the uterus.</p
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