A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.

A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images

We present an automatic segmentation method using the Maximum a posterior (MAP)-Markov random field (MRF) framework that possesses regional adaptive capability for the segmentation of MR brain images with the presence of noise and the intensity inhomogeneity. A spatial-varying Gaussian mixture (SVGM) is used to model the conditional probability distribution of each of the three brain tissues (WM, GM and CSF), and the MRF is used to represent prior probabilities. A three-component random vector consisting of spatial and intensity information is used in SVGM such that the model can represent more complex image characteristics, such as intensity inhomogeneity. The regional adaptive capability is achieved by imposing a similarity criterion that minimizes the intensity variation of the segmented tissues in local regions. Initial parameters of SVGM are estimated from a brain atlas using either the expectation maximization (EM) algorithm or modified k-mean (MKM) algorithm, and the iterated conditional modes (ICM) algorithm is used to perform the segmentation. The parameters estimation and the ICM algorithm are alternatively operated until a stopping criterion is reached. The implement of the method is validated by quantitatively comparing with other published classification methods on fifteen simulated and twenty in vivo brain volumes using the same evaluation criteria. Our algorithm demonstrates superior performance in all cases other than two, and is especially suitable for the volumes with higher noise and inhomogeneity. The complete experiments illustrate that the incorporations of the spatial information and the regional adaptive processing into MAP-MRF framework provide better solution to the segmentation of MR brain images.

DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

As magnetic resonance imaging (MRI) is an important technology of radiological evaluation and computer- aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation of brain MR Images based on t-mixture model is outlined follows. The parameters of t-mixture model for the image are firstly estimated. Then the posterior probabilities of the pixels of the image are computed. At last, the image is segmented according to the Bayes decision rule for minimum error. Experimental results show that t-mixture model fits for medical image segmentation.

Manual segmentation of Magnetic Resonance Images (MRI) is a time-consuming process, thus automatic segmentation of brain MR images has attracted more attention in recent years. In this paper, we introduce Dynamic Classifier Selection Markov Random Field (DCSMRF) algorithm for supervised segmentation of brain MR images into three main tissues such as White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). DCSMRF combines a novel ensemble method with the Markov Random Field (MRF) algorithm and tries to obtain the advantages of both algorithms. For the ensemble part of DCSMRF, we propose an ensemble method called Dynamic Classifier System-Weighted Local Accuracy (DCS-WLA) which is a type of Combination of Multiple Classifier (CMC) algorithm. Later, the MRF algorithm is utilized for incorporating spatial, contextual and textural information in this paper. For the MRF section, an energy function based on the output of the DCS-WLA algorithm is proposed, then maximum value for Maximum A Posterior (MAP) criterion is searched to obtain optimal segmentation. The MRF algorithm applies similar to a post processing step in which only a subset of pixels is selected for optimization step. Hence, a vast amount of search space is pruned. Consequently, the computational burden of the proposed algorithm is more tolerable than the conventional MRF-based methods. Moreover, by employing ensemble algorithms, the accuracy and reliability of final results are enhanced compared to the individual methods.

The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.

It needs set parameters on image segmentation based on PCNN (Pulse Coupled Neural Network) now. This paper points out the new method for medical image segmentation based on improved PCNN and Tsallis entropy. The new methods can automatically segment the medical images without selecting the PCNN parameters. It gets the best results with combining with the Tsallis entropy. The new method is very useful for PCNN application in the medical images segmentation.

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

A queue-based region growing algorithm for accurate segmentation of multi-dimensional digital images

Hidden Markov random field model based brain MR image segmentation using clonal selection algorithm and Markov chain Monte Carlo method

LCP-Net: A local context-perception deep neural network for medical image segmentation

The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms (P < 0.05). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M (H = 14.200, P = 0.001), but there was no great difference from the group C and the group W (P > 0.05). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M (H = 12.800, P = 0.002), and there was no statistical difference between the group C and W (P > 0.05). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.

Event Abstract Back to Event Study of cerebral asymmetry in schizophrenia based on an novel automatic cerebral hemisphere segmentation method for MRI Lu Zhao1*, Ulla Ruotsalainen1, Jussi Hirvonen2, Jarmo Hietala3 and Jussi Tohka1 1 Tampere University of Technology, Finland 2 Turku PET Centre, Finland 3 University of Turku, Finland In this work, we statistically analyzed the cerebral volume asymmetry for schizophrenic and healthy populations by employing a novel and completely automatic cerebral hemisphere segmentation method for MRI. The segmentation method is based on the information of partial volume effect (PVE) and the partial differential equations based shape bottlenecks (PDE-SB) algorithm [1]. PVE is an inevitable problem of MRI, but it is rarely taken into account by the existing automatic MR brain image segmentation techniques. The PDE-SB algorithm can detect the bottlenecks connecting two parts of a complex object by simulating a steady state of the information transmission process between the two parts with partial differential equations. Our method consists of two major stages: compartmental brain decomposition [2] and cerebral hemisphere segmentation [3]. Firstly, a partial volume estimation approach [4] is used to obtain the partial volume voxel classification [into grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), CSF/GM, GM/WM and CSF/background] and estimate the amount of each tissue type in each brain voxel. Then the WM+GM/WM region is split into compartmental seeds corresponding to cerebrum, cerebellum and brainstem utilizing the PDE-SB algorithm. The brain volume is compartmentally decomposed by reconstructing the original shapes of the three compartments from the compartmental seeds according to the compartmental boundary knowledge defined with partial volume information. The cerebrum portion is extracted from the decomposed brain volume, and the cerebral hemispheres are segmented with the PDE-SB algorithm and the partial volume estimation results. In this work, this method was applied to a set of clinical T1-weighted MR images of 18 schizophrenic (11 males, 7 females) and 19 healthy (12 males, 7 females) subjects. All subjects were right-handed, and the schizophrenic patients were never medicated. With the obtained PVE information and cerebral hemisphere segmentation, the asymmetry index (AI) was calculated for cerebrum, cerebral GM and cerebral WM. AI was defined as (VR-VL)/[1/2(VR+VL)]×100, where VR and VL were the volume sizes of the right and left hemispheres respectively. The permutation test was conducted to evaluate the statistical significance of the difference of acquired AIs between schizophrenic and healthy populations. Here, the null hypothesis was defined as that AIs of schizophrenic and healthy populations had identical probability distribution, and the significance level was selected as 0.05. According to the statistical analysis results, the cerebral GM volume asymmetry of female schizophrenic population had significantly different probability distribution from the female healthy population's (p=0.002). There were no significant difference between schizophrenic and healthy populations with regard to volume asymmetry in the other cases (p>0.05). Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Poster Presentation Topic: Neuroimaging Citation: Zhao L, Ruotsalainen U, Hirvonen J, Hietala J and Tohka J (2008). Study of cerebral asymmetry in schizophrenia based on an novel automatic cerebral hemisphere segmentation method for MRI. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.069 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: Lu Zhao, Tampere University of Technology, Tampere, Finland, lu.zhao@tut.fi Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka Google Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka Google Scholar Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka PubMed Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Interactive segmentation methods utilize user-provided positive and negative clicks to guide the model in accurately segmenting target objects. Compared to fully automatic medical image segmentation, these methods can achieve higher segmentation accuracy with limited image data, demonstrating significant potential in clinical applications. Typically, for each new click provided by the user, conventional interactive segmentation methods reprocess the entire network by re-inputting the click into the segmentation model, which greatly increases the user’s interaction burden and deviates from the intended goal of interactive segmentation tasks. To address this issue, we propose an efficient segmentation network, ESM-Net, for interactive medical image segmentation. It obtains high-quality segmentation masks based on the user’s initial clicks, reducing the complexity of subsequent refinement steps. Recent studies have demonstrated the strong performance of the Mamba model in various vision tasks; however, its application in interactive segmentation remains unexplored. In our study, we incorporate the Mamba module into our framework for the first time and enhance its spatial representation capabilities by developing a Spatial Augmented Convolution (SAC) module. These components are combined as the fundamental building blocks of our network. Furthermore, we designed a novel and efficient segmentation head to fuse multi-scale features extracted from the encoder, optimizing the generation of the predicted segmentation masks. Through comprehensive experiments, our method achieved state-of-the-art performance on three medical image datasets. Specifically, we achieved 1.43 NoC@90 on the Kvasir-SEG dataset, 1.57 NoC@90 on the CVC-ClinicDB polyp segmentation dataset, and 1.03 NoC@90 on the ADAM retinal disk segmentation dataset. The assessments on these three medical image datasets highlight the effectiveness of our approach in interactive medical image segmentation.