Automatic Ultrasound Image Segmentation Framework Based on Darwinian Particle Swarm Optimization

Develop and evaluate a deep learning-based automatic meningioma segmentation method for preoperative meningioma differentiation using radiomic features. A retrospective multicentre inclusion of MR examinations (T1/T2-weighted and contrast-enhanced T1-weighted imaging) was conducted. Data from centre 1 were allocated to training (n = 307, age = 50.94 ± 11.51) and internal testing (n = 238, age = 50.70 ± 12.72) cohorts, and data from centre 2 external testing cohort (n = 64, age = 48.45 ± 13.59). A modified attention U-Net was trained for meningioma segmentation. Segmentation accuracy was evaluated by five quantitative metrics. The agreement between radiomic features from manual and automatic segmentations was assessed using intra class correlation coefficient (ICC). After univariate and minimum-redundancy-maximum-relevance feature selection, L1-regularized logistic regression models for differentiating between low-grade (I) and high-grade (II and III) meningiomas were separately constructed using manual and automatic segmentations; their performances were evaluated using ROC analysis. Dice of meningioma segmentation for the internal testing cohort were 0.94 ± 0.04 and 0.91 ± 0.05 for tumour volumes in contrast-enhanced T1-weighted and T2-weighted images, respectively; those for the external testing cohort were 0.90 ± 0.07 and 0.88 ± 0.07. Features extracted using manual and automatic segmentations agreed well, for both the internal (ICC = 0.94, interquartile range: 0.88-0.97) and external (ICC = 0.90, interquartile range: 0.78-70.96) testing cohorts. AUC of radiomic model with automatic segmentation was comparable with that of the model with manual segmentation for both the internal (0.95 vs. 0.93, p = 0.176) and external (0.88 vs. 0.91, p = 0.419) testing cohorts. The developed deep learning-based segmentation method enables automatic and accurate extraction of meningioma from multiparametric MR images and can help deploy radiomics for preoperative meningioma differentiation in clinical practice. • A deep learning-based method was developed for automatic segmentation of meningioma from multiparametric MR images. • The automatic segmentation method enabled accurate extraction of meningiomas and yielded radiomic features that were highly consistent with those that were obtained using manual segmentation. • High-grade meningiomas were preoperatively differentiated from low-grade meningiomas using a radiomic model constructed on features from automatic segmentation.

Detection of ischemic stroke using brain magnetic resonance imaging (MRI) images is vital and a challenging task in clinical practice. We propose a novel method based on optimization technique to identify stroke lesion in diffusion-weighted imaging (DWI) MRI sequences of the brain. The algorithm was tested in a specific slice having large area of stroke region from a series of 292 real-time images obtained from different stroke affected subjects from IMS and SUM Hospital. The proposed method consists of pre-processing, segmentation, extraction of important features and classification of stroke type. The particle swarm optimization (PSO) and Darwinian particle swarm optimization (DPSO) algorithms were applied in segmenting the stroke lesions. The important features were extracted with the gray-level co-occurrence matrix (GLCM) algorithm and in decision making process, the feature set is classified into three types of stroke according to The Oxfordshire Community Stroke Project (OCSP) classification using support vector machine (SVM) classifier. The lesion area was segmented effectively with DPSO process with classification weighted accuracy of 90.23%, which is higher than PSO method having weighted accuracy of 85.19%. Similarly, the values of different measured parameters were high in DPSO technique, the computational time was also higher in DPSO method for segmenting the stroke lesions. These results confirm that the DPSO-based approach with SVM classifier is an effective way to identify the decision making process of ischemic stroke lesion in MRI images of the brain.

Image segmentation is a platform for many high-level applications. It aims to identify the different homogeneous regions of an image. In our work, we cast the problem as a parallel distributed optimization task and we present a new method for image segmentation by multilevel thresholding based on generalized island model (GIM). This model is characterized by the cooperation of three metaheuristics namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC). The cooperation is ensured by the use of a migration pattern the purpose of which is to avoid getting stuck in local optima. The effectiveness of the proposed method has been assessed and compared to each of the algorithms of the GIM model when used separately and other methods from the literature like Darwinian PSO (DPSO), Fractional-Order Darwinian PSO (FO-DPSO). The obtained results are very promising and show the positive impact of the use of a parallel distributed model. They also show that the proposed method competes and even outperforms the other methods.

Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels. All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.

To compare choroidal neovascularization (CNV) area and vessel density (VD) measurements between two different swept-source optical coherence tomography angiography (SS-OCTA) devices. En face optical coherence tomography angiography (OCTA) images of patients affected by neovascular age-related macular degeneration were collected prospectively from two devices: Zeiss PLEX Elite 9000 (Carl Zeiss Meditec, Dublin, CA) and Topcon DRI OCT Triton SS-OCTA (Topcon, Tokyo, Japan). Choroidal neovascularization area and VD of images were measured and analyzed with ImageJ software by two readers to evaluate the agreement between two devices, with respect to different image size (3 × 3 and 6 × 6 mm) and different image segmentation (automatic vs. manual), and a Topcon equivalent Zeiss segmentation as control (i.e., the equivalent anatomical slab given by Topcon device on the Zeiss device). A total of 30 eyes (30 patients) were analyzed. There was an excellent agreement between the two readers in CNV area measurements intraclass correlation coefficient >0.9 in all analyses. We found excellent agreement in CNV area measurements (manual and automatic segmentations) when comparing 3 × 3-mm or 6 × 6-mm images both for each single device and between the two devices (overall intraclass correlation coefficient > 0.9). Vessel density measurements between manual to automatic segmentation within the same device and same image size had a high intraclass correlation coefficient value, but there was a poor agreement in VD between different image sizes (3 × 3 mm vs. 6 × 6 mm) in the same device and also comparing the two devices (3 × 3 Topcon vs. 3 × 3 Zeiss; 6 × 6 Topcon vs. 6 × 6 Zeiss). There was a poor agreement between the Topcon equivalent Zeiss segmentation and all other segmentations. There was an excellent agreement in CNV area measurements for both swept-source optical coherence tomography angiography devices in automatic and manual segmentations. However, the Topcon equivalent Zeiss segmentation was not comparable with any of the preset segmentations of Topcon and Zeiss devices. There was a poor agreement in CNV VD between different image size and different devices. For these reasons, it seems that, for accurate longitudinal analysis of VD, it is better to use the same device for each individual, even if both devices can be used interchangeably for CNV area measurements using automatic or manual segmentations.

The satellite image segmentation is a key area for current research and numerous work has been done for exploration of this area. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a literature review of different nature based optimization algorithms such as modified artificial bee colony (MABC) algorithm, ABC algorithm, particle swarm optimization (PSO), Darwinian PSO, genetic algorithm (GA), Wind driven optimization (WDO) and cuckoo search(CS) using different objective functions has been discussed to find the optimized multilevel thresholds. These nature influenced optimization methods and their performances are compared using different objective functions for optimal multilevel thresholding. The comparative study shows that the MABC algorithm and different variants of CS algorithm are very strong and accurate in results generating using image segmentation. Both methods search multilevel thresholds very efficiently and correctly, and in MABC an improved bee’s search solution are used and in CS the cuckoo search solution are used.

Cardiac MRI has experienced a crescent relevance in clinical investigations. The segmentation of myocardial walls is a prerequisite for assessment of cardiac viability. Manual or semi-automatic segmentation of all the images of a subject is tedious, as well as consuming much time from cardiologists. In this study, we selected 23 slices of simulated cardiac MR by MRXCAT and 30 real slices of CINE-MR from 15 patients with Chagas Disease. The proposed pipeline of the fully automatic segmentation consists of three steps: 1. Preprocessing; 2. Automatic Seeds Definition; and 3. Segmentation by Geodesic Active Contour. An experienced cardiologist provided the gold standard annotations of apical, mid-ventricular and basal LV myocardium. We use the following three metrics to validate the proposed pipeline with different signal to noise ratio: Dice similarity (DS), Precision (Pr) and Volumetric Similarity (VS). DS show good agreement between manual segmentation and the automatic segmentation in simulated images with SNR 200, 25, 15 and 5, i.e., 0.98, 0.93, 0.9 and 0.72, respectively. We found moderate agreements between manual segmentation and Snake segmentation in simulated images with SNR 200, 25, 15 and 5, i.e., 0.38, 0.42, 0.34 and 0.39, respectively. The DS, VS, and Pr obtained suggest substantial agreements between the manual and our proposed method segmentation in images of Chagas’s Disease, i.e., 0.8 [0.69–0.87], 0.89 [0.72–0.99] and 0.9 [0.76–0.98] (mean [min–max]), respectively. Our findings suggest that one can use the proposed method in the automatic myocardium segmentation with reliability similar to manual tracing, although completely free of human interaction.

Automatic segmentation of the choroid on optical coherence tomography (OCT) images helps ophthalmologists in diagnosing eye pathologies. Compared to manual segmentations, it is faster and is not affected by human errors. The presence of the large speckle noise in the OCT images limits the automatic segmentation and interpretation of them. To solve this problem, a new curvelet transform-based K-SVD method is proposed in this study. Furthermore, the dataset was manually segmented by a retinal ophthalmologist to draw a comparison with the proposed automatic segmentation technique. In this study, curvelet transform-based K-SVD dictionary learning and Lucy-Richardson algorithm were used to remove the speckle noise from OCT images. The Outer/Inner Choroidal Boundaries (O/ICB) were determined utilizing graph theory. The area between ICB and outer choroidal boundary was considered as the choroidal region. The proposed method was evaluated on our dataset and the average dice similarity coefficient (DSC) was calculated to be 92.14% ± 3.30% between automatic and manual segmented regions. Moreover, by applying the latest presented open-source algorithm by Mazzaferri et al. on our dataset, the mean DSC was calculated to be 55.75% ± 14.54%. A significant similarity was observed between automatic and manual segmentations. Automatic segmentation of the choroidal layer could be also utilized in large-scale quantitative studies of the choroid.

Separate renal function assessment is important in clinical decision making. The single-photon emission computed tomography is commonly used for the assessment although radioactive, tedious and of high cost. This study aimed to automatically assess the separate renal function using plain CT images and artificial intelligence methods, including deep learning-based automatic segmentation and radiomics modeling. We performed a retrospective study on 281 patients with nephrarctia or hydronephrosis from two centers (Training set: 159 patients from Center I; Test set: 122 patients from Center II). The renal parenchyma and hydronephrosis regions in plain CT images were automatically segmented using deep learning-based U-Net transformers (UNETR). Radiomic features were extracted from the two regions and used to build radiomic signature using the ElasticNet, then further combined with clinical characteristics using multivariable logistic regression to obtain an integrated model. The automatic segmentation was evaluated using the dice similarity coefficient (DSC). The mean DSC of automatic kidney segmentation based on UNETR was 0.894 and 0.881 in the training and test sets. The average time of automatic and manual segmentation was 3.4s/case and 1477.9s/case. The AUC of radiomic signature was 0.778 in the training set and 0.801 in the test set. The AUC of the integrated model was 0.792 and 0.825 in the training and test sets. It is feasible to assess the renal function of each kidney separately using plain CT and AI methods. Our method can minimize the radiation risk, improve the diagnostic efficiency and reduce the costs.

Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques

Tumor computed tomography (CT) image segmentation can provide a basis for the determination of tumor location and type. Therefore, it is of great significance to study the automatic segmentation method of tumor CT images. To address the problem of poor segmentation effect of traditional automatic tumor CT images segmentation methods, we propose an automatic segmentation method for tumor CT images using deep convolutional neural networks (DCNNs). First, the CT tumor image is simplified. According to the features of the target region and the background region, the distribution features of the tumor region in the CT images are obtained by convolution calculation, and the feature extraction is completed by feature fusion. Second, based on the feature extraction results, a deep supervised network is constructed to determine the image depth, which lays a solid foundation for accurate segmentation of tumor regions. Finally, DCNN was used to construct automatic segmentation for tumor CT images, which achieves the automatic segmentation of tumor CT images by mode calculation. The results show that the segmented tumor region is close to the actual region and the maximum pixel loss coefficient is 0.07, the maximum segmentation sensitivity is 7865[Formula: see text]kbps/s, the pixel segmentation specific coefficient and the segmentation edge distance are kept at a low level, which has a certain application value in the field of tumor CT images.

To apply deep convolution neural network to the segmentation task in myocardial arterial spin labeled perfusion imaging and to develop methods that measure uncertainty and that adapt the convolution neural network model to a specific false-positive versus false-negative tradeoff. The Monte Carlo dropout U-Net was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and regional myocardial blood flow were available for comparison. We consider 2 global uncertainty measures, named "Dice uncertainty" and "Monte Carlo dropout uncertainty," which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter β was used to adapt the model to a specific false-positive versus false-negative tradeoff. The Monte Carlo dropout U-Net achieved a Dice coefficient of 0.91 ± 0.04 on the test set. Myocardial blood flow measured using automatic segmentations was highly correlated to that measured using the manual segmentation (R2 = 0.96). Dice uncertainty and Monte Carlo dropout uncertainty were in good agreement (R2 = 0.64). As β increased, the false-positive rate systematically decreased and false-negative rate systematically increased. We demonstrate the feasibility of deep convolution neural network for automatic segmentation of myocardial arterial spin labeling, with good accuracy. We also introduce 2 simple methods for assessing model uncertainty. Finally, we demonstrate the ability to adapt the convolution neural network model to a specific false-positive versus false-negative tradeoff. These findings are directly relevant to automatic segmentation in quantitative cardiac MRI and are broadly applicable to automatic segmentation problems in diagnostic imaging.

We present a robust, automated, model-based segmentation method for kidney MR Images. We used dynamic programming and a minimal path approach to detect the optimal path within a weighted graph between two end points. We used an energy function to combine distance and gradient information to guide the marching curve and thus evaluate the best path and span a broken edge. We developed an algorithm to automate the placement of initial end points. Dynamic programming was used to automatically optimize and update end points in the procedure for searching curves. A deformable 3D model was generated using principle component analysis (PCA) and it was used as the prior knowledge for the selection of initial end points and for the evaluation of the best path. We used our minimal path method with surface models to segment mouse kidneys slice-by-slice. The method has been tested for kidney MR images of 44 mice. To quantitatively assess the automatic segmentation method, we compared the automatic segmentation results with manual segmentation. The average and standard deviation of the overlap ratios is 0.93 + 0.05. The distance error between the automatic and manual segmentation is 0.85 plusmn 0.41 pixel. The 3D automatic minimal path segmentation method is fast, accurate, and robust. It provides a useful tool for quantification and characterization of kidney MR images.

Automatic three-dimensional analysis of posterosuperior full-thickness rotator cuff tear size on MRI

BackgroundWhole brain atrophy as measured from T1‐wieghted MR images is a key biomarker for progression in Alzheimer's disease (AD) and employed as marker for treatment efficacy in clinical trials. The Boundary Shift Integral (BSI) methodology (Freborough;IEEE;1997) is the widely established standard and well validated method for volume change. BSI requires initialisation with a whole‐brain segmentation at both timepoints, manual segmentations remain the gold standard, however the BSI approach is robust to minor segmentation, potentially facilitating the use of automatic segmentation approaches. Furthermore, to improve sensitivity to volume change and exploit the probabilistic nature of many automatic segmentations a generalised form of the BSI (gBSI) pipeline has been proposed (Prados;NeurobiolAging;2015). The use of a fully automatic pipeline to estimate volume change provides a scalable solution for clinical trial deployment without the burden of manual segmentation or edits and associated variability introduced. Here we assess the use of LEAP (Ledig;ProcISBI;2012), a fully automatic whole brain segmentation tool providing both binary and probabilistic parcellations, to initialise the BSI, as implemented in MIDAS, and gBSI pipelines.MethodWhole brain volume change over 1 year was estimated for 48 participants, acquired as part of a clinical trial, with the BSI and gBSI methods initialised with 1) manually generated whole brain segmentations and 2) automatically estimated whole brain segmentations generate with LEAP. No manual corrections were performed; all LEAP segmentations passed a visual quality control inspection.ResultFigure 1 shows the relationship between the manually initialised BSI and the fully‐automated LEAP‐BSI approach. Table 1 shows average measures of volume change across all tested methods. All measures were significantly correlated with non‐significant differences in reported mean volume change.ConclusionLEAP offers segmentations suitable for initialisation of the BSI method providing a pipeline for fully automatic volume estimation consistent with the semi‐automated BSI approach, providing a scalable solution for clinical trial deployment without the associated variability and resource burden required for fully manual segmentations. Furthermore the probabilistic output of LEAP allows for the use of the gBSI methodology with the associated potential of increased sensitivity to volume change.