Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.     

An Active Contour Model for the Segmentation of Images with Intensity Inhomogeneities and Bias Field Estimation

Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.     

A Novel Dictionary Based Computer Vision Method for the Detection of Cell Nuclei

Cell nuclei detection in fluorescent microscopic images is an important and time consuming task in a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make individual nuclei detection a challenging task for automated image analysis. This paper proposes a novel and robust detection method based on the active contour framework. Improvement over conventional approaches is achieved by exploiting prior knowledge of the nucleus shape in order to better detect individual nuclei. This prior knowledge is defined using a dictionary based approach which can be formulated as the optimization of a convex energy function. The proposed method shows accurate detection results for dense clusters of nuclei, for example, an F-measure (a measure for detection accuracy) of 0.96 for the detection of cell nuclei in peripheral blood mononuclear cells, compared to an F-measure of 0.90 achieved by state-of-the-art nuclei detection methods.     

Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images

Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.     

Medical image segmentation based on level set and isoperimetric constraint

Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.     

基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法。该算法在Chan—Vese（CV）模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘。将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明：该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取。     

Multi-Atlas Based Adaptive Active Contour Model with Application to Organs at Risk Segmentation in Brain MR Images

BackgroundAccurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images.MethodsThe proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour.ResultsExperimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy.ConclusionsThe geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.     

基于动态方向梯度矢量流模型的脑肿瘤图像分割

目的:针对GVF Snake模型算法收敛容易陷入局部极小值及对初始轮廓位置敏感等缺点,提出一种动态方向梯度矢量流模型(DDGVF),使其更适合医学图像的分割。方法:利用主动轮廓模型的提取和跟踪特定区域内目标轮廓的方法,将其应用于医学图像如CT、MRI和超声图像的处理,以获取特定器官及组织的轮廓。结果:动态方向梯度矢量流场(DDGVF)能够较好地提取出脑肿瘤图像。结论:利用该方法能够较好地分割提取出脑肿瘤图像的肿瘤病变区域,为进一步对其纹理和形状等特征进行描述和分析提供了可靠的依据。     

Comparing proteins by their unfolding pattern

Single molecule force spectroscopy has evolved into an important and extremely powerful technique for investigating the folding potentials of biomolecules. Mechanical tension is applied to individual molecules, and the subsequent, often stepwise unfolding is recorded in force extension traces. However, because the energy barriers of the folding potentials are often close to the thermal energy, both the extensions and the forces at which these barriers are overcome are subject to marked fluctuations. Therefore, force extension traces are an inadequate representation despite widespread use particularly when large populations of proteins need to be compared and analyzed. We show in this article that contour length, which is independent of fluctuations and alterable experimental parameters, is a more appropriate variable than extension. By transforming force extension traces into contour length space, histograms are obtained that directly represent the energy barriers. In contrast to force extension traces, such barrier position histograms can be averaged to investigate details of the unfolding potential. The cross-superposition of barrier position histograms allows us to detect and visualize the order of unfolding events. We show with this approach that in contrast to the sequential unfolding of bacteriorhodopsin, two main steps in the unfolding of the enzyme titin kinase are independent of each other. The potential of this new method for accurate and automated analysis of force spectroscopy data and for novel automated screening techniques is shown with bacteriorhodopsin and with protein constructs containing GFP and titin kinase.     

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application – SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary “Manual Initialize” and “Hand Draw” tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.     

Modelling Temporal Stability of EPI Time Series Using Magnitude Images Acquired with Multi-Channel Receiver Coils

In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR₀). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custom-made image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter κ which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR′₀) measured directly from root-sum-of-squares reconstructed magnitude images so that κ = SNR′₀/SNR₀ (under the condition of SNR₀>50 and number of channels ≤32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter κ was validated using an independent measure of the actual SNR₀ for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels.     

Force spectroscopy and fluorescence microscopy of dsDNA–YOYO-1 complexes: implications for the structure of dsDNA in the overstretching region

When individual dsDNA molecules are stretched beyond their B-form contour length, they reveal a structural transition in which the molecule extends 1.7 times its contour length. The nature of this transition is still a subject of debate. In the first model, the DNA helix unwinds and combined with the tilting of the base pairs (which remain intact), results in a stretched form of DNA (also known as S-DNA). In the second model the base pairs break resulting effectively in two single-strands, which is referred to as force-induced melting. Here a combination of optical tweezers force spectroscopy with fluorescence microscopy was used to study the structure of dsDNA in the overstretching regime. When dsDNA was stretched in the presence of 10 nM YOYO-1 an initial increase in total fluorescence intensity of the dye–DNA complex was observed and at an extension where the dsDNA started to overstretch the fluorescence intensity leveled off and ultimately decreased when stretched further into the overstretching region. Simultaneous force spectroscopy and fluorescence polarization microscopy revealed that the orientation of dye molecules did not change significantly in the overstretching region (78.0°± 3.2°). These results presented here clearly suggest that, the structure of overstretched dsDNA can be explained accurately by force induced melting.     

A multi-step directional generalized gradient vector flow snake for target tumor segmentation in US-guided high-intensity focused ultrasound ablation

Getting precise locations of target tumors can help to ensure ablation of cancerous tissues and avoid unwanted destruction of healthy tissues in high-intensity focused ultrasound (HIFU) treatment system. Because of speckle noise and spurious boundaries in ultrasound images, traditional image segmentation methods are not suitable for achieving the precise locations of target tumors in HIFU ablation. In this paper, a multi-step directional generalized gradient vector flow snake model is introduced for target tumor segmentation. In the first step, the traditional generalized gradient vector flow (GGVF) snake is used to obtain an approximate contour of the tumor. According to the approximate contour, a new distance map is generated. Subsequently, a new directional edge map is created by calculating a scalar product of the gradients of the distance map and the initial image. In this process, the gradient directional information and the magnitude information of the distance map are used to attenuate unwanted edges and highlight the real edges in the new directional edge map. Finally, a refined GGVF field is derived from a diffusion operation of the gradient vectors of the directional edge map. The GGVF field is used to refine the tumor's contour, by directing the approximate contour to edges with the desired gradient directionality. Based on the newly developed snake model, the influences of the spurious boundaries and the speckle noise are significantly reduced in the ultrasound image segmentation. Experimental results indicate that this technique is greatly useful for target tumor segmentation in HIFU treatment system     

Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method

Automatic image segmentation of immunohistologically stained breast tissue sections helps pathologists to discover the cancer disease earlier. The detection of the real number of cancer nuclei in the image is a very tedious and time consuming task. Segmentation of cancer nuclei, especially touching nuclei, presents many difficulties to separate them by traditional segmentation algorithms. This paper presents a new automatic scheme to perform both classification of breast stained nuclei and segmentation of touching nuclei in order to get the total number of cancer nuclei in each class. Firstly, a modified geometric active contour model is used for multiple contour detection of positive and negative nuclear staining in the microscopic image. Secondly, a touching nuclei method based on watershed algorithm and concave vertex graph is proposed to perform accurate quantification of the different stains. Finally, benign nuclei are identified by their morphological features and they are removed automatically from the segmented image for positive cancer nuclei assessment. The proposed classification and segmentation schemes are tested on two datasets of breast cancer cell images containing different level of malignancy. The experimental results show the superiority of the proposed methods when compared with other existing classification and segmentation methods. On the complete image database, the segmentation accuracy in term of cancer nuclei number is over than 97%, reaching an improvement of 3–4% over earlier methods.     

Contour extraction of Drosophila embryos

Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blurred embryo boundaries. We test the proposed framework on a data set of 8,000 embryonic images, and achieve promising accuracy (88 percent), that is, substantially higher than the-state-of-the-art results.     

UV light-damaged DNA and its interaction with human replication protein A: an atomic force microscopy study

We have imaged a non-damaged and UV-damaged DNA fragment and its complexes with human replication protein A (RPA) using tapping mode atomic force microscopy (AFM). For imaging, molecules were immobilized under nearly physiological conditions on mica surfaces. Quantitative sizing of the 538 bp DNA before and after UV light treatment shows a reduction in the contour and persistence lengths and mean square end-to-end distance as a consequence of UV irradiation. Complexes of the UV-damaged DNA with RPA, an essential component of the initial steps of nucleotide excision repair, can be detected at high resolution with AFM and reveal conformational changes of the DNA related to complex formation. By phase image analysis we are able to discriminate between protein and DNA in the complexes. The DNA molecules are found to ‘wrap’ around the RPA, which in turn results in a considerable reduction in its apparent contour length.     

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.     

Magnetic microbubble-mediated ultrasound-MRI registration based on robust optical flow model

Background

As a dual-modality contrast agent, magnetic microbubbles (MMBs) can not only improve contrast of ultrasound (US) image, but can also serve as a contrast agent of magnetic resonance image (MRI). With the help of MMBs, a new registration method between US image and MRI is presented.

Methods

In this method, MMBs were used in both ultrasound and magnetic resonance imaging process to enhance the most important information of interest. In order to reduce the influence of the speckle noise to registration, semi-automatic segmentations of US image and MRI were carried out by using active contour model. After that, a robust optical flow model between US image segmentation (floating image) and MRI segmentation (reference image) was built, and the vector flow field was estimated by using the Coarse-to-fine Gaussian pyramid and graduated non-convexity (GNC) schemes.

Results

Qualitative and quantitative analyses of multiple group comparison experiments showed that registration results using all methods tested in this paper without MMBs were unsatisfactory. On the contrary, the proposed method combined with MMBs led to the best registration results.

Conclusion

The proposed algorithm combined with MMBs contends with larger deformation and performs well not only for local deformation but also for global deformation. The comparison experiments also demonstrated that ultrasound-MRI registration using the above-mentioned method might be a promising method for obtaining more accurate image information.

     

Motion-based mechanisms of illusory contour synthesis

Neurophysiological studies and computational models of illusory contour formation have focused on contour orientation as the underlying determinant of illusory contour shape in both static and moving displays. Here, we report a class of motion-induced illusory contours that demonstrate the existence of novel mechanisms of illusory contour synthesis. In a series of experiments, we show that the velocity of contour terminations and the direction of motion of a partially occluded figure regulate the perceived shape and apparent movement of illusory contours formed from moving image sequences. These results demonstrate the existence of neural mechanisms that reconstruct occlusion relationships from both real and inferred image velocities, in contrast to the static geometric mechanisms that have been the focus of studies to date.