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.     

Quantitative MRI water content mapping of porcine intervertebral disc during uniaxial compression

Background: Intervertebral disc (IVD) diseases are major public health problem in industrialized countries where they affect a large proportion of the population. In particular, IVD degeneration is considered to be one of the leading causes of pain consultation and sick leave. The aim of this study was to develop a new method for assessing the functionality of IVD in order to diagnose IVD degeneration. Methods: For this purpose, we have designed a specific device that enables to mechanically load porcine IVD ex vivo in the 4.7-Tesla horizontal superconducting magnet of a magnetic resonance (MR) scanner. Proton density weighted imaging (ρ_H-MRI) of the samples was acquired. Findings: The post-processing on MR images allowed (1) to reconstruct the 3D deformation under a known mechanical load and (2) to infer the IVD porosity assuming an incompressible poroelastic model. Interpretation: This study demonstrates the ability to follow the change in morphology and hydration of an IVD using MR measurements, thereby providing valued information for a better understanding of IVD function.     

Loss and re-adaptation of lumbar intervertebral disc water signal intensity after prolonged bedrest

The adaptation and re-adaptation process of the intervertebral disc (IVD) to prolonged bedrest is important for understanding IVD physiology and IVD herniations in astronauts. Little information is available on changes in IVD composition. In this study, 24 male subjects underwent 60-day bedrest and In/Out Phase magnetic resonance imaging sequences were performed to evaluate IVD shape and water signal intensity. Scanning was performed before bedrest (baseline), twice during bedrest, and three, six and twenty-four months after bedrest. Area, signal intensity, average height, and anteroposterior diameter of the lumbar L3/4 and L4/5 IVDs were measured. At the end of bedrest, disc height and area were significantly increased with no change in water signal intensity. After bedrest, we observed reduced IVD signal intensity three months (p=0.004 versus baseline), six months (p=0.003 versus baseline), but not twenty-four months (p=0.25 versus baseline) post-bedrest. At these same time points post-bedrest, IVD height and area remained increased. The reduced lumbar IVD water signal intensity in the first months after bedrest implies a reduction of glycosaminoglycans and/or free water in the IVD. Subsequently, at two years after bedrest, IVD hydration status returned towards pre-bedrest levels, suggesting a gradual, but slow, re-adaptation process of the IVD after prolonged bedrest.     

Semi-automatic Method for Low-Grade Gliomas Segmentation in Magnetic Resonance Imaging

Background: Analyzing MR scans of low-grade glioma, with highly accurate segmentation will have an enormous potential in neurosurgery for diagnosis and therapy planning. Low-grade gliomas are mainly distinguished by their infiltrating character and irregular contours, which make the analysis, and therefore the segmentation task, more difficult. Moreover, MRI images show some constraints such as intensity variation and the presence of noise.Methods: To tackle these issues, a novel segmentation method built from the local properties of image is presented in this paper. Phase-based edge detection is estimated locally by the monogenic signal using quadrature filters. This way of detecting edges is, from a theoretical point of view, intensity invariant and responds well to the MR images. To strengthen the tumor detection process, a region-based term is designated locally in order to achieve a local maximum likelihood segmentation of the region of interest. A Gaussian probability distribution is considered to model local images intensities.Results: The proposed model is evaluated using a set of real subjects and synthetic images derived from the Brain Tumor Segmentation challenge –BraTS 2015. In addition, the obtained results are compared to the manual segmentation performed by two experts. Quantitative evaluations are performed using the proposed approach with regard to four related existing methods.Conclusion: The comparison of the proposed method, shows more accurate results than the four existing methods.     

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.     

TRUS image segmentation with non-parametric kernel density estimation shape prior

Due to noises, speckles, etc., automatic prostate segmentation is rather challenging, and using only low-level information such as intensity gradient is insufficient and unable to tackle the problem. In this paper, we propose an automatic prostate segmentation method combining intrinsic properties of TRUS images with the high-level shape prior information. First, intrinsic properties of TRUS images, such as the intensity transition near the prostate boundary as well as the speckle induced texture features obtained by Gabor filter banks, are integrated to deform the model to the target contour. These properties make our method insensitive to high gradient regions introduced by noises and speckles. Then, the preliminary segmentation is fine-tuned by the non-parametric shape prior, which is optimally distilled by non-parametric kernel density estimation as it can approximate arbitrary distributions. The refinement is along the direction of mean shift vector, and considerably strengthens the robustness of the method. The performance of our method is validated by experimental results. Compared with the state of the art, the accuracy and robustness of the method is quite promising, and the mean absolute distance is only 1.21 ± 0.85 mm.     

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PurposeIn this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.MethodsOur method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.ResultsWe tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.ConclusionThe proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.     

Effect of molecular weight distribution on the solution properties of sodium hyaluronate in 0.2M NaCl solution

Various molecular parameters, which characterize sodium hyaluronate in 0.2M NaCl solution, were obtained at 25°C by means of the static and dynamic light scattering and low shear viscometry over the molecular weight range of 5.94–627 × 10⁴. Molecular weight distribution was obtained by using the Laplace inversion method of the autocorrelation function of the scattered light intensity and by Yamakawa theory for the wormlike chain with the stiff chain parameters for sodium hyaluronate in 0.2M NaCl (persistence length, chain diameter, molar mass per unit contour length, and the excluded‐volume strength). The molecular weight distribution thus obtained reproduced the solution properties of sodium hyaluronate well. Especially, the intrinsic viscosity showed a good agreement over four orders of molecular weight with Yamakawa theory combined with the Barrett function. Sodium hyaluronate in 0.2M NaCl solution is well expressed by the wormlike chain model affected by the excluded‐volume effect with the persistence length of 4.2 nm. © 1999 John Wiley & Sons, Inc. Biopoly 50: 87–98, 1999     

Segmentation of parotid glands from registered CT and MR images

PurposeTo develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.MethodsMagnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.ResultsUsing the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.ConclusionsAutomatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.     

Monoclonal antibody A7 coupled to magnetic particles as a contrast enhancing agent for magnetic resonance imaging of human colorectal carcinoma

Background: Local recurrence, the most frequent pattern of recurrence of rectal carcinoma, is almost always fatal. The difficulty of diagnosing local recurrence contributes importantly to the poor prognosis. Methods: We coupled monoclonal antibody (Mab) A7, which reacts specifically with human colorectal carcinoma, to ferromagnetic lignosite (FML) particles to distinguish rectal carcinoma from other tissues by magnetic resonance (MR) imaging. We examined retention of immunoreactivity by the A7-FML complexes in vitro, and also their distribution in vivo according to radiolabeling and MR imaging when injected into nude mice bearing human colorectal carcinoma xenografts. Results: A7-FML retained binding activity nearly identical to that of Mab A7. Significantly more ¹²⁵I-labeled A7-FML accumulated in engrafted tumors than did ¹²⁵I-labeled normal mouse IgG-FML complexes (P<0.05). A7-FML disappeared rapidly from the blood. Normal tissues accumulated less ¹²⁵I-labeled A7-FML than tumors; this accumulation decreased linearly with time. In MR imaging, signal intensity was reduced in the tumor by the injection of A7-FML. Conclusions: A7-FML is potentially useful as a MR contrast enhancing agent for human colorectal carcinoma xenografts implanted subcutaneously.     

Statistical shape model-based prediction of tibiofemoral cartilage

Abstract

Computed tomography is used more routinely to design patient-specific instrumentation for knee replacement surgery. Its moderate imaging cost and simplified segmentation reduce design costs compared with magnetic resonance (MR) imaging, but it cannot provide the necessary cartilage information. Our method based on statistical shape modelling proved to be successful in predicting tibiofemoral cartilage in leave-one-out experiments. The obtained accuracy of 0.54?mm for femur and 0.49?mm for tibia outperforms the average cartilage thickness distribution and reported inter-observer MR segmentation variability. These results suggest that shape modelling is able to predict tibiofemoral cartilage with sufficient accuracy to design patient-specific instrumentation.     

A comparison on effects of normalisations in the detection of differentially expressed genes

Background  

Various normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation; intensity dependent normalisation; and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.     

Magnetic resonance image and blood manganese concentration as indices for manganese content in the brain of rats

Neurological disorders similar to parkinsonian syndrome and signal hyperintensity in brain on T₁-weighted magnetic resonance (MR) images have been reported in patients receiving long-term total parenteral nutrition (TPN). These symptoms have been associated with manganese (Mn) depositions in brain. Although alterations of signal intensity on T₁-weighted MR images in brain and of Mn concentration in blood are theoretically considered good indices for estimating Mn deposition in brain, precise correlations between these parameters have not been demonstrated as yet. Male Sprague-Dawley rats received TPN with 10-fold the clinical dose of the trace element preparation (TE-5) for 7 d. At 0, 2, 4, 6, and 8 wk post-TPN, the cortex, striatum, midbrain, and cerebellum were evaluated by MR images, and Mn concentration in blood and Mn content in these brain sites were measured by atomic absorption spectrometry. Immediately after TPN termination, signal hyperintensity in brain sites and elevated Mn content in blood and brain sites were observed. These values recovered at 4 wk post-TPN. A positive correlation was observed between either the signal intensity in certain brain sites or Mn content in blood and the relevant brain sites. Our observations suggest that the Mn concentration in blood and signal intensity in the brain sites on T₁-weighted MR images are reliable indices for monitoring Mn contents in brain.     

Characterization of Cre recombinase mouse lines enabling cell type-specific targeting of postnatal intervertebral discs

Cre/loxP technology is an important tool for studying cell type-specific gene functions. Cre recombinase mouse lines, including Agc1-CreER^T2, Col2a1-Cre; Col2a1-CreER^T2, Shh-Cre, Shh-CreER^T2, and Osx-Cre, have been proven to be valuable tools to elucidate the biology of long bones, yet the information for their activity in postnatal intervertebral disc (IVD) tissues was very limited. In this study, we used R26-mTmG fluorescent reporter to systematically analyze cell specificity and targeting efficiency of these six mouse lines in IVD tissues at postnatal growing and adult stages. We found that Agc1-CreER^T2 is effective to direct recombination in all components of IVDs, including annulus fibrosus (AF), nucleus pulposus (NP), and cartilaginous endplate (CEP), upon tamoxifen induction at either 2 weeks or 2 months of ages. Moreover, Col2a1-Cre targets most of the cells in IVDs, except for some cells in the outer AF (OAF) and NP. In contrast, the activity of Col2a1-CreER^T2 is mainly limited to the IAF of IVD tissues at either stage of tamoxifen injection. Similarly, Shh-Cre directs recombination specifically in all NP cells, whereas Shh-CreER^T2 is active only in a few NP cells when tamoxifen is administered at either stage. Finally, Osx-Cre targets cells in the CEP, but not in the NP or AF of IVDs tissues at these two stages. Thus, our data demonstrated that all these Cre lines can direct recombination in IVD tissues at postnatal stages with different cell type specificity and/or targeting efficiency, and can, therefore, serve as valuable tools to dissect cell type-specific gene functions in IVD development and homeostasis.     

EFFECTS OF SELECTED PHYSICOCHEMICAL FACTORS ON GROWTH AND ZOOSPOROGENESIS OF CLADOPHORA GLOMERATA (CHLOROPHYTA)1

The effects of vitamin B₁ and B₁₂, temperature, light intensity and photoperiod on dry weight production and zoosporogenesis of Cladophora glomerata (L.)Kütz. were investigated by factorial experiments. Statistical analysis showed all of the above factors to be significantly (P < 0.01) influenced by these two parameters. Zoosporogenesis appeared to be more sensitive to vitamin limitation than dry weight production. Dry weight production and zoosporogenesis exhibited significantly different maxima. Dry weight production was most strongly influenced by temperature whereas photoperiod exerted the strongest influence on zoosporogenesis. It was possible to experimentally separate the light intensity and photoperiod factors; analysis showed photoperiod to be the primary factor influencing zoosporogenesis. Among the conditions tested, an 8:16 h LD short day photoperiod elicited the greatest number of zoosporangia.     

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.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Biological Image Segmentation Using Region-Scalable Fitting Energy with B-Spline Level Set Implementation and Watershed

ObjectivesImage segmentation plays an important role in the analysis and understanding of the cellular process. However, this task becomes difficult when there is intensity inhomogeneity between regions, and it is more challenging in the presence of the noise and clustered cells. The goal of the paper is propose an image segmentation framework that tackles the above cited problems.Material and methodsA new method composed of two steps is proposed: First, segment the image using B-spline level set with Region-Scalable Fitting (RSF) active contour model, second apply the Watershed algorithm based on new object markers to refine the segmentation and separate clustered cells. The major contributions of the paper are: 1) Use of a continuous formulation of the level set in the B-spline basis, 2) Develop the energy function and its derivative by introducing the RSF model to deal with intensity inhomogeneity, 3) For the Watershed, propose a relevant choice of markers that considers the cell properties.ResultsExperimental results are performed on widely used synthetic images, in addition to simulated and real biological images, without and with additive noise. They attest the high quality of segmentation of the proposed method in terms of quantitative and qualitative evaluation.ConclusionThe proposed method is able to tackle many difficulties at the same time: overlapped intensities, noise, different cell sizes and clustered cells. It provides an efficient tool for image segmentation especially biological ones.