Sand microscopic image segmentation with enhanced tuna swarm optimization exponential entropy

The morphological analysis of dendritic spines is an important challenge for the neuroscientific community. Most state-of-the-art techniques rely on user-supervised algorithms to segment the spine surface, especially those designed for light microscopy images. Therefore, processing large dendritic branches is costly and time-consuming. Although deep learning (DL) models have become one of the most commonly used tools in image segmentation, they have not yet been successfully applied to this problem. In this article, we study the feasibility of using DL models to automatize spine segmentation from confocal microscopy images. Supervised learning is the most frequently used method for training DL models. This approach requires large data sets of high-quality segmented images (ground truth). As mentioned above, the segmentation of microscopy images is time-consuming and, therefore, in most cases, neuroanatomists only reconstruct relevant branches of the stack. Additionally, some parts of the dendritic shaft and spines are not segmented due to dyeing problems. In the context of this research, we tested the most successful architectures in the DL biomedical segmentation field. To build the ground truth, we used a large and high-quality data set, according to standards in the field. Nevertheless, this data set is not sufficient to train convolutional neural networks for accurate reconstructions. Therefore, we implemented an automatic preprocessing step and several training strategies to deal with the problems mentioned above. As shown by our results, our system produces a high-quality segmentation in most cases. Finally, we integrated several postprocessing user-supervised algorithms in a graphical user interface application to correct any possible artifacts.

Hybrid architectures based on convolutional neural networks and Transformers, effectively captures both the local details and the overall structural context of lesion tissues and cells, achieving highly competitive segmentation results in microscopic hyperspectral image (MHSI) segmentation tasks. However, the fixed tokenization schemes and single-dimensional feature extraction and fusion in existing methods lead to insufficient global feature extraction in hyperspectral pathology images. Base on this, we propose a multi-scale token divided and spatial-channel fusion transformer network (MT-SCnet) for MHSIs segmentation. Specifically, we first designed a Multi-Scale Token Divided module. It divides token at different scale based on mirror padding and promotes information interaction and fusion between different tokens to obtain more representative features for subsequent global feature extraction. Secondly, a novel spatial channel fusion transformer was designed to capture richer features from spatial and channel dimensions, and eliminates the semantic gap between features from different dimensions based on cross-attention fusion block. Additionally, to better restore spatial information, deformable convolutions were introduced in decoder. The Experiments on two MHSI datasets demonstrate that MT-SCnet outperforms the comparison methods. This advance has significant implications for the field of MHSIs segmentation. Our code is freely available at https://github.com/sharycao/MT-SCnet.

We have been developing a computer-aided diagnosis (CAD) system for automatically recognizing cervical cancer cells from Papanicolaou smear. Considering that pathological changes of cervix can be indicated by the abnormity of the nucleus of intermediate cell, the key task of this system is to find the intermediate cells and segment the nucleus precisely. This paper presents a novel approach for automatic segmentation of microscopic cervical cell images using multispectral imaging techniques. In order to capture images at different wavelengths, a Liquid Crystal Tunable Filter (LCTF) device is used to provide wavelength selection from 400nm to 720nm with an increment of 10nm. Considering the spectral variances of background, nucleus and cytoplasm, background is extracted firstly from the microscopic images by calculating pixel intensity variance at 470nm, 530nm, 570nm, 580nm and 650nm. Then superficial cells are extracted apart from intermediate cells easily at 530nm 650nm because of the different pixel intensity distribution of the two kinds of cells at these two wavelengths. To segment the nucleus from intermediate cells, we adopt two procedures. Firstly, the nuclei are roughly segmented apart by using an iterative maximum deviation between-cluster algorithm. Secondly, a novel rigorous algorithm based on active contour model is adopted to achieve more exact nuclei segmentation. Using the method proposed in this paper, we did experiments on over 300 cervical smears, and the results show that this method is more robust and precise.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection

The study of medical image analysis encompasses the various techniques for acquisition of images of biological structures pertaining to human body using radiations in different frequency ranges. The advancements in medical imaging over the past decades are enabling physicians to non-invasively peer inside the human body for the purpose of diagnosis and therapy. In this chapter, the objective is to focus on the studies relating to the analysis of endoscopic images of lower esophagus for abnormal region detection and identification of cancerous growth. Several color image segmentation techniques have been developed for automatic detection of cancerous regions in endoscopic images, which assists the physician for faster, proper diagnosis and treatment of the disease. These segmentation methods are evaluated for comparing their performances in different color spaces, namely, RGB, HSI, YCbCr, HSV, and CIE Lab. The segmented images are expected to assist the medical expert in drawing the biopsy samples precisely from the detected pathological legions. Further, various methods have been proposed for segmentation and classification of squamous cell carcinoma (SCC) from color microscopic images of esophagus tissue during pathological investigation. The efficacy of these methods has been demonstrated experimentally with endoscopic and microscopic image set and compared with manual segmentation done by medical experts. It is envisaged that the research in this direction eventually leads to the design and production of efficient intelligent computer vision systems for assisting the medical experts in their task of speedy accurate diagnosis of diseases and prescription of appropriate treatment of the patients.

This paper is concerned with quantitative analysis of color biomedical images. We take into account microscopic cytological and histological images. The aim of the examination is to extract two types of object from a set of images and evaluate their count and area. An essential step in the recognition procedure is image segmentation, due to the poor quality of the source images. We test some segmentation techniques including the combination of color discrimination and region based methods.

Object density-based image segmentation and its applications in biomedical image analysis

In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for developing an automated application for the early diagnostics of lymphatic system tumours. The proposed method follows a two-step approach to, firstly, find the nuclei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus. Experimental results have shown the feasibility of the method.KeywordsMicroscopic Cell ImagesColor Image SegmentationFuzzy ClusteringArtificial Neural Networks

This paper presents research related to segmentation based on supervisory control, at multiple levels, of optimization of parameters of segmentation methods, and adjustment of 3D microscopic images, with the aim of creating a more efficient segmentation approach. The challenge is how to improve the segmentation of 3D microscopic images using known segmentation methods, but without losing processing speed. In the first phase of this research, a model was developed based on an ensemble of 11 segmentation methods whose parameters were optimized using genetic algorithms (GA). Optimization of the ensemble of segmentation methods using GA produces a set of segmenters that are further evaluated using a two‐stage voting system, with the aim of finding the best segmenter configuration according to multiple criteria. In the second phase of this research, the final segmenter model is developed as a result of two‐level optimization. The best obtained segmenter does not affect the speed of image processing in the exploitation process as its operating speed is practically equal to the processing speed of the basic segmentation method. Objective selection and fine‐tuning of the segmenter was done using multiple segmentation methods. Each of these methods has been subject to an intensive process of a significant number of two‐stage optimization cycles. The metric has been specifically created for objective analysis of segmenter performance and was used as a fitness function during GA optimization and result validation. Compared to the expert manual segmentation, segmenter score is 99.73% according to the best mean segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set). Segmenter score is 99.49% according to the most stable segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set and considering the reference image classes MGTI median, MGTI voter and GGTI).

In the last years, it has become very popular as well as significant to diagnose patients as less invasively as only it is possible. This becomes extremely important mainly when diagnosing adolescent patients or even babies. This paper describes the possibilities of medical diagnostics from a different point of view which is the evaluation of microscopic biomedical images of patient's body instead of examining him directly, i.e., invasively. The subject of research is human respiratory system and its diseases which are often caused or closely connected with proper function of small microscopic structures which should serve to clean one's airways. Such structures are called cilia of respiratory epithelium and can be found within nose, trachea or bronchi. The old way of diagnostic of a patient who is suspected to suffer from a disease called Primary ciliary dyskinesia (PCD), as he often undergoes serious inflammations and other breathing complications, comprises several repeated bronchoscopies which cut a small pieces of epithelium from selected parts of respiratory apparatus that are consequently observed using microscope. This is very uncomfortable and many times very hard to perform as the most affected group of patients are children under the age of three. Moreover, evaluation of microscopic samples is often imprecise because doctors assess it subjectively and the proper frequency of ciliary movement is only estimated and classified “slow” or “quick enough”, without accurate value. This paper proposes automated, much faster and much more exact ways of frequency measurements of ciliary movement based on processing and segmentation of biomedical microscopic images, which means more precise evaluation of patient's ciliary health status without such invasiveness as it is used during older methods of examination.

In developmental biology, quantitative tools to extract features from fluorescence microscopy images are becoming essential to characterize organ morphogenesis at the cellular level. However, automated image analysis in this context is a challenging task, owing to perturbations induced by the acquisition process, especially in organisms where the tissue is dense and opaque. We propose an automated framework for the segmentation of 3D microscopy images of highly cluttered environments such as developing tissues. The approach is based on a partial differential equation framework that jointly takes advantage of the nuclear and cellular membrane information to enable accurate extraction of nuclei and cells in dense tissues. This framework has been used to study the developing mouse heart, allowing the extraction of quantitative information such as the cell cycle duration; the method also provides qualitative information on cell division and cell polarity through the creation of 3D orientation maps that provide novel insight into tissue organization during organogenesis.

SummaryWe introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines.Availability and implementationYeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation.Supplementary informationSupplementary data are available at Bioinformatics online.

We present a high performance variant of the popular geodesic active contours which are used for splitting cell clusters in microscopy images. Previously, we implemented a linear pipelined version that incorporates as many cues as possible into developing a suitable level-set speed function so that an evolving contour exactly segments a cell/nuclei blob. We use image gradients, distance maps, multiple channel information and a shape model to drive the evolution. We also developed a dedicated seeding strategy that uses the spatial coherency of the data to generate an over complete set of seeds along with a quality metric which is further used to sort out which seed should be used for a given cell. However, the computational performance of any level-set methodology is quite poor when applied to thousands of 3D data-sets each containing thousands of cells. Those data-sets are common in confocal microscopy. In this work, we explore methods to stream the algorithm in shared memory, multi-core environments. By partitioning the input and output using spatial data structures we insure the spatial coherency needed by our seeding algorithm as well as improve drastically the speed without memory overhead. Our results show speed-ups up to a factor of six.

This paper proposes an automated data-driven integrated circuit segmentation approach of scan electron microscopy (SEM) images inspired by state-of-the-art CNN-based image perception methods. Based on the requirements derived from real industry applications, we take wire segmentation and via detection algorithms to generate integrated circuit segmentation maps from SEMs in our approach. On SEM images collected in the industrial applications, our method achieves an average of 50.71 on Electrically Significant Difference (ESD) in the wire segmentation task and 99.05% F1 score in the via detection task, which achieves about 85% and 8% improvements over the reference method, respectively.

We develop a novel method for image segmentation of 3D confocal microscopy images of emerging hematopoietic stem cells. The method is based on the theory of persistent homology and uses an optimal threshold to select the most persistent cycles in the persistence diagram. This enables the segmentation of the image's most contrasted and representative shapes. Coupling this segmentation method with a meshing algorithm, we define a pipeline for 3D reconstruction of confocal volumes. Compared to related methods, this approach improves shape segmentation, is more ergonomic to automatize, and has fewer parameters. We apply it to the segmentation of membranes, at subcellular resolution, of cells involved in the endothelial-to-hematopoietic transition (EHT) in the zebrafish embryos.