Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping

Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements

Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation approaches and methods. In this paper, we suggest a novel variational method for joint restoration and segmentation of noisy images which are having intensity and inhomogeneity in the existence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and resolved through alternating direction minimization approach of multipliers. Through experiments, we have tested the performance of the suggested approach on diverse types of synthetic and real images in the existence of intensity and in-homogeneity; and evaluate the precision, as well as, the robustness of the suggested model. Furthermore, the outcomes are, then, compared with other state-of-the-art models including two-phase and multi-phase approaches and show that our method has superiority for images in the existence of noise and inhomogeneity. Our empirical evaluation and experiments, using real images, evaluate and assess the efficiency of the suggested model against several other closest rivals. We observed that the suggested model can precisely segment all the images having brightness, diffuse edges, high contrast light in the background, and inhomogeneity.

Abdomen tissues segmentation from computed tomography images using deep learning and level set methods

Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surrounding organs. In this paper, an attention-based deep learning method for automated abdomen tissues segmentation is proposed. In our method, image cropping is first applied to the original images. U-net model with attention mechanism is then constructed to obtain the initial abdomen tissues. Finally, level set evolution which consists of three energy terms is used for optimize the initial abdomen segmentation. The proposed model is evaluated across 470 subsets. For liver segmentation, the mean dice are 96.2 and 95.1% for the FLARE21 datasets and the LiTS datasets, respectively. For kidney segmentation, the mean dice are 96.6 and 95.7% for the FLARE21 datasets and the LiTS datasets, respectively. Experimental evaluation exhibits that the proposed method can obtain better segmentation results than other methods.

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections, and squeeze-expand convolutional layers from the fire module to segment brain MRI data. The proposed SM-SegNet architecture involves a multi-scale deep network on the encoder side and deep supervision on the decoder side, which uses combined-connections (skip connections and pooling indices) from the encoder to the decoder layer. The multi-scale side input layers support the deep network layers' extraction of discriminative feature information, and the decoder side provides deep supervision to reduce the gradient problem. By using combined-connections, extracted features can be transferred from the encoder to the decoder resulting in recovering spatial information, which makes the model converge faster. Long skip connections were used to stabilize the gradient updates in the network. Owing to the adoption of the fire module, the proposed model was significantly faster to train and offered a more efficient memory usage with 83% fewer parameters than previously developed methods, owing to the adoption of the fire module. The proposed method was evaluated using the open-access series of imaging studies (OASIS) and the internet brain segmentation registry (IBSR) datasets. The experimental results demonstrate that the proposed SM-SegNet architecture achieves segmentation accuracies of 95% for cerebrospinal fluid, 95% for gray matter, and 96% for white matter, which outperforms the existing methods in both subjective and objective metrics in brain MRI segmentation.

Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models

The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.

A Novel Combined Level Set Model for Carpus Segmentation from Magnetic Resonance Images with Prior Knowledge aligned in Polar Coordinate System

BACKGROUND AND OBJECTIVE

Segmentation on carpus provides essential information for clinical applications including pathological evaluations, therapy planning, wrist biomechanical analysis, etc. Along with the acquisition procedure of magnetic resonance (MR) technique, poor quality of wrist images (e.g., occlusion, low signal-to-noise ratio, and contrast) often causes segmentation failure.

METHODS

In this work, to address such problems, a shape prior enhanced level set model was proposed. By transferring a shape contour in Cartesian Coordinate System (COS) into a curve in Polar Coordinate System (POS), parameters describing conventional shape invariance, i.e., translations, rotation, and scale were simplified into a single parameter for phase shift, which strongly improved algorithm efficiency. Given a training set in COS, a confidence interval representing the corresponding curves in POS was utilized as the shape prior set term in the model. Integrated with an edge detector, a local intensity descriptor, and a regularization term, the proposed method further possessed abilities against noise, intensity inhomogeneity as well as re-initialization problem. Images from 15 in-vivo acquired MR-datasets of the human wrist were used for validation. The performance of the proposed method has been compared with three state-of-the-art methods.

RESULTS

We reported a Dice Similarity Coefficient of 96.88±1.20%, a Relative Volume Difference of -1.53±3.01%, a Volume Overlap Error of 6.03±2.23%, a 95% Hausdorff Distance of 1.43±0.66 mm, an Average Symmetric Surface Distance of 0.50±0.17 mm, and a Root Mean Square Distance of 0.71±0.25 mm for the proposed method. The time consumption was 36.03±19.98 s.

CONCLUSIONS

Experimental results indicated that, compared with three other methods, the proposed method achieved significant improvement in terms of accuracy and efficiency.

ACTIVE CONTOUR MODELS-BASED SEGMENTATION OF LEFT VENTRICLE IN ULTRASOUND IMAGES FOR DIFFERENT AXES VIEWS

Segmentation of the left ventricle in ultrasound images for viewing through different axes is a critical aspect. This paper proposes the development of novel active contour models with shape constraint to segment the left ventricle in three different axis views of the ultrasound images. The shapes observed in all the axis views of the left ventricle were not similar. According to the cardiac cycle, the valve opening in the end-diastolic phase influenced the left ventricle segmentation; hence, a shape constraint was embedded in the active contour model to keep ventricle’s shape, especially in the Apical long-axis view and Apical four-chamber view. Furthermore, for different axes views, diverse active contour models were proposed to fit each situation. The shape constraint in each function for different views exhibited a specific shape during the function iteration. In order to speed up the algorithm evolution, previous results were used for the initialization of the present active contour. We evaluated the proposed method on 57 patients with three different views: Apical long-axis view, Apical four-chamber view and Short-axis view at the papillary muscle level and obtained the Dice similarity coefficients of [Formula: see text], [Formula: see text] and [Formula: see text] and the Hausdorff distance metrics of [Formula: see text], [Formula: see text] and [Formula: see text], respectively. The qualitative and quantitative evaluations showed an advantage of our method in terms of segmentation accuracy.

A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

BACKGROUND

Intensity inhomogeneity is one of the common artifacts in image processing. This artifact makes image segmentation more challenging and adversely affects the performance of intensity-based image processing algorithms.

NEW METHOD

In this paper, a novel region-based level set method is proposed for segmenting the images with intensity inhomogeneity with applications to brain tumor segmentation in magnetic resonance imaging (MRI) scans. For this purpose, the inhomogeneous regions are first modeled as Gaussian distributions with different means and variances, and then transferred into a new domain, where preserves the Gaussian intensity distribution of each region but with better separation. Moreover, our method can perform bias field correction. To this end, the bias field is represented by a linear combination of smooth base functions that enables better intensity inhomogeneity modeling. Therefore, level set fundamental formulation and bias field are modified in the proposed approach.

RESULTS

To assess the performance of the proposed method, different inhomogeneous images, including synthetic images as well as real brain magnetic resonance images from BraTS 2017 dataset are segmented. Being evaluated by Dice, Jaccard, Sensitivity, and Specificity metrics, the results show that the proposed method suppresses the side effect of the over-smoothing object boundary and it has good accuracy in the segmentation of images with extreme intensity non-uniformity. The mean values of these metrics in brain tumor segmentation are 0.86 ± 0.03, 0.77 ± 0.05, 0.94 ± 0.04, 0.99 ± 0.003, respectively.

COMPARISON WITH EXISTING METHOD(S)

Our method were compared with six state-of-the-art image segmentation methods: Chan-Vese (CV), Local Intensity Clustering (LIC), Local iNtensity Clustering (LINC), Global inhomogeneous intensity clustering (GINC), Multiplicative Intrinsic Component Optimization (MICO), and Local Statistical Active Contour Model (LSACM) models. We used qualitative and quantitative comparison methods for segmenting synthetic and real images. Experiments indicate that our proposed method is robust to noise and intensity non-uniformity and outperforms other state-of-the-art segmentation methods in terms of bias field correction, noise resistance, and segmentation accuracy.

CONCLUSIONS

Experimental results show that the proposed model is capable of accurate segmentation and bias field estimation simultaneously. The proposed model suppresses the side effect of the over-smoothing object boundary. Moreover, our model has good accuracy in the segmentation of images with extreme intensity non-uniformity.

Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method

BACKGROUND AND OBJECTIVE

Brain tumor segmentation is a challenging issue due to noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual MRI segmentation is a very tedious, time-consuming, and user-dependent task. This paper aims to presents a novel level set method to address aforementioned challenges for reliable and automatic brain tumor segmentation.

METHODS

In the proposed method, a new functional, based on level set method, is presented for medical image segmentation. Firstly, we define a superpixel fuzzy clustering objective function. To create superpixel regions, multiscale morphological gradient reconstruction (MMGR) operation is used. Secondly, a novel fuzzy energy functional is defined based on superpixel segmentation and histogram computation. Then, level set equations are obtained by using gradient descent method. Finally, we solve the level set equations by using lattice Boltzmann method (LBM). To evaluate the performance of the proposed method, both synthetic image dataset and real Glioma brain tumor images from BraTS 2017 dataset are used.

RESULTS

Experiments indicate that our proposed method is robust to noise, initialization, and intensity non-uniformity. Moreover, it is faster and more accurate than other state-of-the-art segmentation methods with the averages of running time is 3.25 seconds, Dice and Jaccard coefficients for automatic tumor segmentation against ground truth are 0.93 and 0.87, respectively. The mean value of Hausdorff distance, Mean absolute Distance (MAD), accuracy, sensitivity, and specificity are 2.70, 0.005, 0.9940, 0.9183, and 0.9972, respectively.

CONCLUSIONS

Our proposed method shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results. Moreover, our method is fast and robust to noise, initialization, and intensity non-uniformity. Since most of the medical images suffer from these problems, the proposed method can more effective for complicated medical image segmentation.

Frangi based multi-scale level sets for retinal vascular segmentation

Retinal vascular disease has always been the focus of medical attention. However, segmentation of the retinal vessels from fundus images is still an open problem due to intensity inhomogeneity in the image and thickness diversity of the retinal vessels. In this paper, we propose Frangi based multi-scale level sets to segment retinal vessels from fundus images. Vascular structures are first enhanced by the Frangi filter with local optimal scales being obtained at the same time. The enhanced image and local optimal scales are taken considered as inputs of the proposed level set models. Effectiveness of the proposed multi-scale level sets to their scale fixed versions has been evaluated using DRIVE and STARE image repositories. In addition, the proposed level set models have been tested on the DRIVE and STARE images. Experiments show that the proposed models produce segmentation accuracy at the same level with state-of-the-art methods.

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System

Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.

Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain

OBJECTIVE

Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.

MATERIALS AND METHODS

The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain.

RESULTS

The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (- 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (- 5.6 ± 7.6%, p < 0.001, effect size: 0.73).

DISCUSSION

Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

OBJECTIVE

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features.

MATERIALS AND METHODS

T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ.

RESULTS

The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005).

CONCLUSION

A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.