MR image segmentation of the knee bone using phase information

Magnetic resonance (MR) imaging is a widely available and well accepted non invasive imaging technique. Development of automatic and semi-automatic techniques to analyse MR images has been the focus of much research and numerous publications. However, most of this research only uses the magnitude of the acquired complex MR signal, discarding the phase information. In MR, the phase relates to the magnetic properties of tissues, information which is not found in the magnitude signal. As a result, phase is a complement to the magnitude signal and can improve the segmentation and analysis of MR images. In this paper, we consider the automatic classification of textured tissues in 3D MRI. Specifically, we include features extracted from the phase of the MR signal to improve texture discrimination in the bone segmentation. Our approach does not require phase unwrapping, with the MR signal processed in its complex form. The extra information extracted from the phase provides better segmentation, compared to only using magnitude features. The segmentation approach is integrated within a novel multiscale scheme, designed to improve the speed of pixel based classification algorithms, such as support vector machines. An order of magnitude increase is obtained, by reducing the number of pixels that need to be classified.     

HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation

High performance of deep learning models on medical image segmentation greatly relies on large amount of pixel-wise annotated data, yet annotations are costly to collect. How to obtain high accuracy segmentation labels of medical images with limited cost (e.g. time) becomes an urgent problem. Active learning can reduce the annotation cost of image segmentation, but it faces three challenges: the cold start problem, an effective sample selection strategy for segmentation task and the burden of manual annotation. In this work, we propose a Hybrid Active Learning framework using Interactive Annotation (HAL-IA) for medical image segmentation, which reduces the annotation cost both in decreasing the amount of the annotated images and simplifying the annotation process. Specifically, we propose a novel hybrid sample selection strategy to select the most valuable samples for segmentation model performance improvement. This strategy combines pixel entropy, regional consistency and image diversity to ensure that the selected samples have high uncertainty and diversity. In addition, we propose a warm-start initialization strategy to build the initial annotated dataset to avoid the cold-start problem. To simplify the manual annotation process, we propose an interactive annotation module with suggested superpixels to obtain pixel-wise label with several clicks. We validate our proposed framework with extensive segmentation experiments on four medical image datasets. Experimental results showed that the proposed framework achieves high accuracy pixel-wise annotations and models with less labeled data and fewer interactions, outperforming other state-of-the-art methods. Our method can help physicians efficiently obtain accurate medical image segmentation results for clinical analysis and diagnosis.     

Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains

This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. Segmentation by intensity-based nonrigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four different approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average-shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). The MUL strategy is a novel application of multi-classifier techniques, which are common in pattern recognition, to atlas-based segmentation. For each atlas selection strategy, the segmentation performance of the algorithm was quantified by the similarity index (SI) between the automatic segmentation result and a manually generated gold standard. The best segmentation accuracy was achieved using the MUL paradigm, which resulted in a mean similarity index value between manual and automatic segmentation of 0.86 (AVG, 0.84; SIM, 0.82; IND, 0.81). The superiority of the MUL strategy over the other three methods is statistically significant (two-sided paired t test, P < 0.001). Both the MUL and AVG strategies performed better than the best possible SIM and IND strategies with optimal a posteriori atlas selection (mean similarity index for optimal SIM, 0.83; for optimal IND, 0.81). Our findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy.     

On the convergence of EM-like algorithms for image segmentation using Markov random fields

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation–maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.     

图像分割在医学图像中的研究方法及应用

图像分割是指将一幅图像分解为若干互不交迭区域的集合,是图像处理与机器视觉的基本问题之一.医学图像分割是图像分割的一个重要应用领域,也是一个经典难题.本文从应用的特定角度,对近年来医学图像分割的新方法或改进算法进行综述,并简要讨论了每类分割方法的特点及应用.     

Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming

This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.     

A review of 3D vessel lumen segmentation techniques: Models,features and extraction schemes

Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies.Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task.Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.     

Active learning for training sample selection in remote sensing image classification using spatial information

Collection of training samples for remote sensing image classification is always time-consuming and expensive. In this context, active learning (AL) that aims at using limited training samples to achieve promising classification performances is developed. Recently, integration of spatial information into AL exhibits new potential for image classification. In this letter, an AL approach with two-stage spatial computation (AL-2SC) is proposed to improve the selection of training samples. The spatial features derived from remote sensing image and the probability outputs from the neighboring pixels are introduced in AL process. Moreover, we compare several AL approaches which take spatial information into account. In experiments, random sampling (RS) and four AL methods, including AL using breaking ties heuristic (BT), AL with spatial feature (AL-SF), AL with neighbouring responses (AL-NR), and AL-2SC, are considered. Three remote sensing datasets, including one hyperspectral and two multispectral images, are used to compare the performance of different methods. It is illustrated that, the utilization of spatial information is very important for the improvement of AL performance, and the proposed AL-2SC shows the most satisfactory result.     

A method for segmentation of dental implants and crestal bone

Purpose   Medical imaging and in particular digital radiographic images offer a great deal of information to dentists in the clinical diagnosis and treatment processes on a daily basis. This paper presents a new method aimed to produce an accurate segmentation of dental implants and the crestal bone line in radiographic images. With this, it is possible computing several measures to biomechanical and clinical evaluation of dental implants positioning and evolution. Methods   The proposed segmentation method includes two major steps: (1) the preprocessing that combine denoising filters, morphological operations and histogram threshold techniques and (2) the final segmentation involving made-to-measure adjusted and trained active shape models for detecting the precise location of the intended structures. Results   Resulting measurements were compared to manual measurements made by experts on representative radiographs from patients. The calculated intraclass correlation coefficient was 0.75 and showed good reliability of the method, and the Bland-Altman analysis showed 95 % of the values within the limits of agreement. The mean of the differences between the manual and method-driven measurements was 0.049 mm ( $-0.137; -0.040$ ) 95 % CI, inferior to the established limit (0.15mm). Conclusions   It was demonstrated that the method achieved a precise segmentation of the intended structures. The validation process on standardized periapical radiographs showed good agreement between the manual measurements and the ones produced by the new method. Future work will be focused on making the method more robust to densitometry changes and to validate the method on non-standardized radiographs.     

A hybrid approach for segmentation of anatomic structures in medical images

Objective

We propose a hybrid interactive approach for the segmentation of anatomic structures in medical images with higher accuracy at lower user interaction cost.

Materials and methods

Eighteen brain MR scans from the Internet Brain Segmentation Repository are used for brain structure segmentation. A MR scan and a CT scan of an old female are used for orbital structure segmentation. The proposed approach combines shape-based interpolation, radial basis function (RBF)-based warping and model-based segmentation. With this approach, to segment a structure in a 3D image, we first delineate the structure in several slices using interactive methods, and then use shape-based interpolation to automatically generate an initial 3D model of the structure from the segmented slices. To refine the initial model, we specify a set of additional points on the structure boundary in the image, and use a RBF to warp the model so that it passes the specified points. Finally, we adopt a point-anchored active surface approach to further deform the model for a better fitting of the model with its corresponding structure in image.

Results

Two brain structures and 15 orbital structures are segmented. For each structure, it needs only to semi- automatically segment three to five 2D slices and specify two to nine additional points on the structure boundary. The time cost for each structure is about 1–3 min. The overlap ratio of the segmentation results and the ground truth is higher than 96%.

Conclusion

The proposed method for the segmentation of anatomic structure achieved higher accuracy at lower user interaction cost, and therefore promising in many applications such as surgery planning and simulation, atlas construction, and morphometric analysis of anatomic structures.     

Evaluation of growth in children using quantitative bone scintigraphy

Our aim was to assess the value of quantitative bone scintigraphy for evaluating long-bone growth and to establish the normal uptake patterns of the growth plate in children. Subjects (180 girls, 154 boys) of normal weight and height, aged 2-20 years, were investigated using skeletal scintigraphy. Regions of interest were outlined over posterior images of the distal femoral growth plate and femoral diaphysis. The average number of counts per fixel in each region of interest was determined. The ratio of uptake in the distal femoral growth plate to that in the femoral diaphysis (GP:D ratio) was calculated for all subjects, and its relationship with age was determined. Peak GP:D ratios in girls and boys were reached at ages 11 and 13 years, respectively (8.26 in girls and 8.18 in boys), corresponding to the periods of most rapid growth. Our findings suggest that determining the scintigraphic GP:D ratio may be useful for evaluating growth and development in children if normal standard GP:D ratios are established.     

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database

The task of segmenting the lung fields, the heart, and the clavicles in standard posterior-anterior chest radiographs is considered. Three supervised segmentation methods are compared: active shape models, active appearance models and a multi-resolution pixel classification method that employs a multi-scale filter bank of Gaussian derivatives and a k-nearest-neighbors classifier. The methods have been tested on a publicly available database of 247 chest radiographs, in which all objects have been manually segmented by two human observers. A parameter optimization for active shape models is presented, and it is shown that this optimization improves performance significantly. It is demonstrated that the standard active appearance model scheme performs poorly, but large improvements can be obtained by including areas outside the objects into the model. For lung field segmentation, all methods perform well, with pixel classification giving the best results: a paired t-test showed no significant performance difference between pixel classification and an independent human observer. For heart segmentation, all methods perform comparably, but significantly worse than a human observer. Clavicle segmentation is a hard problem for all methods; best results are obtained with active shape models, but human performance is substantially better. In addition, several hybrid systems are investigated. For heart segmentation, where the separate systems perform comparably, significantly better performance can be obtained by combining the results with majority voting. As an application, the cardio-thoracic ratio is computed automatically from the segmentation results. Bland and Altman plots indicate that all methods perform well when compared to the gold standard, with confidence intervals from pixel classification and active appearance modeling very close to those of a human observer. All results, including the manual segmentations, have been made publicly available to facilitate future comparative studies.     

基于自动分割技术联合基于体素的形态学观察帕金森病脑灰质结构

目的 探讨基于自动分割技术联合基于体素的形态学(VBM)观察帕金森病(PD)患者全脑灰质异常区域及分布特征的应用价值。方法 基于自动分割技术,应用FIRST工具对29例PD患者(PD组)及30名健康人(对照组)的T1图像皮层下灰质结构精确分割,对比两组各灰质结构体积。并应用VBM方法对两组脑灰质图像进行比较。结果 两组右侧壳核皮层下灰质体积差异有统计学意义(t=10.201,P<0.05)。与对照组比较,PD组脑灰质体积(右侧初级运动皮层,双侧额叶、边缘叶、部分左侧小脑后叶、右侧小脑前叶、右侧小脑后叶、右侧颞叶、顶叶、壳核及左侧枕叶)广泛减少,部分左侧小脑后叶体积增加,两侧半球脑体积缺失不对称(右侧大于左侧)。结论 通过FIRST工具可精确分割并直接计算皮层下灰质结构体积,应用VBM技术可定量分析脑结构形态学异常;二者结合可较全面地表现PD脑灰质体积广泛减少的形态学特点。     

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical image segmentation tasks including myocardial segmentation in cardiac MR images. However, the predicted segmentation maps obtained from such standard CNN do not allow direct quantification of regional shape properties such as regional wall thickness. Furthermore, the CNNs lack explicit shape constraints, occasionally resulting in unrealistic segmentations. In this paper, we use a CNN to predict shape parameters of an underlying statistical shape model of the myocardium learned from a training set of images. Additionally, the cardiac pose is predicted, which allows to reconstruct the myocardial contours. The integrated shape model regularizes the predicted contours and guarantees realistic shapes. We enforce robustness of shape and pose prediction by simultaneously performing pixel-wise semantic segmentation during training and define two loss functions to impose consistency between the two predicted representations: one distance-based loss and one overlap-based loss. We evaluated the proposed method in a 5-fold cross validation on an in-house clinical dataset with 75 subjects and on the ACDC and LVQuan19 public datasets. We show that the two newly defined loss functions successfully increase the consistency between shape and pose parameters and semantic segmentation, which leads to a significant improvement of the reconstructed myocardial contours. Additionally, these loss functions drastically reduce the occurrence of unrealistic shapes in the semantic segmentation output.     

Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.     

Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration

Accurate quantification of the morphology of vessels is important for diagnosis and treatment of cardiovascular diseases. We introduce a new joint segmentation and registration approach for the quantification of the aortic arch morphology that combines 3D model-based segmentation with elastic image registration. With this combination, the approach benefits from the robustness of model-based segmentation and the accuracy of elastic registration. The approach can cope with a large spectrum of vessel shapes and particularly with pathological shapes that deviate significantly from the underlying model used for segmentation. The performance of the approach has been evaluated on the basis of 3D synthetic images, 3D phantom data, and clinical 3D CTA images including pathologies. We also performed a quantitative comparison with previous approaches.     

A dynamic elastic model for segmentation and tracking of the heart in MR image sequences

Strong prior models are a prerequisite for reliable spatio-temporal cardiac image analysis. While several cardiac models have been presented in the past, many of them are either too complex for their parameters to be estimated on the sole basis of MR Images, or overly simplified. In this paper, we present a novel dynamic model, based on the equation of dynamics for elastic materials and on Fourier filtering. The explicit use of dynamics allows us to enforce periodicity and temporal smoothness constraints. We propose an algorithm to solve the continuous dynamical problem associated to numerically adapting the model to the image sequence. Using a simple 1D example, we show how temporal filtering can help removing noise while ensuring the periodicity and smoothness of solutions. The proposed dynamic model is quantitatively evaluated on a database of 15 patients which shows its performance and limitations. Also, the ability of the model to capture cardiac motion is demonstrated on synthetic cardiac sequences. Moreover, existence, uniqueness of the solution and numerical convergence of the algorithm can be demonstrated.