From Pixels to Precision—A Dual-Stream Deep Network for Pathological Nuclei Segmentation

Segmentation accuracy determines the success or failure of computerized analysis procedure in biomedical applications. This paper aims to develop a unique segmentation technique to identify mitotic nuclei from microscopy images of breast histopathology slides. The process involves detection and classification of cell nuclei based on computed features. The proposed method uses Active Contour Model for segmentation of cell nuclei and two versatile classifiers such as Support Vector Machine (SVM) and Random Forest (RF) for classification stage. Segmentation stage provides an accuracy of 95% for cell nuclei. This technique uses a single color channel and a reduced feature set for the whole process. Classification performance is evaluated in terms of sensitivity, specificity, accuracy and F-score measures. Analysis results showed good detection accuracy for RF classifier compared to SVM.

In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variable nuclei in histopathology images. However, a challenging problem during nuclei segmentation is the blocking of cell nuclei, overlapping, and background complexity of the tissue fraction. To address this challenge, we present MIU-net, an efficient deep learning network structure for the nuclei segmentation of histopathology images. Our proposed structure includes two blocks with modified inception module and attention module. The advantage of the modified inception module is to balance the computation and network performance of the deeper layers of the network, combined with the convolutional layer using different sizes of kernels to learn effective features in a fast and efficient manner to complete kernel segmentation. The attention module allows us to extract small and fine irregular boundary features from the images, which can better segment cancer cells that appear disorganized and fragmented. We test our methodology on public kumar datasets and achieve the highest AUC score of 0.92. The experimental results show that the proposed method achieves better performance than other state-of-the-art methods.

Accurate segmentation of nuclei in histopathological images is critical for improving diagnostic precision and advancing computational pathology. Deep learning models employed for this task must effectively handle structural variability while offering transparent and interpretable predictions to ensure clinical reliability. In this study, we investigate the integration of Kolmogorov–Arnold Networks (KANs) into the widely adopted U‐Net architecture, forming a novel hybrid model referred to as U‐KAN. To the best of our knowledge, we are the first to explore the application of U‐KAN for multi‐class nuclei segmentation on the challenging MoNuSAC2020 dataset, leveraging an adaptive sliding window strategy. Our results demonstrate that U‐KAN achieves a 17.9% improvement in Dice coefficient (Dice Similarity Coefficient, DSC) (0.976) and a 25.7% increase in IoU (Intersection over Union) (0.954) compared to baseline method (U‐Net), while also delivering enhanced model interpretability. Gradient‐based explanation techniques further confirm that U‐KAN produces anatomically plausible predictions, with strong attention to nuclear boundaries. These findings suggest that symbolic‐connectionist hybrids like U‐KAN can meaningfully advance automated histopathological image analysis.

Reliable and capable tools are needed for segmentation and classification of nuclei in histopathology images to improve the workflow of histopathologists and lessen the burden of diagnosis. Analyzing histopathology images can be challenging for deep learning models due to the various sources of noise. We propose a pipeline for segmenting and classifying nuclei, trained on a combination of datasets to ensure robustness. This makes our pipeline capable of generalizing on image tiles with varying Hematoxylin & Eosin concentrations. For nuclei classification, we present a novel convolutional neural network classifier that demonstrate state-of-the-art classification performance on the Lizard dataset.KeywordsDeep learningConvolutional neural networksComputational pathologyNuclei segmentationNuclei classificationMedical imaging

With the use of computer-aided diagnostic systems, the automatic detection and segmentation of the cell nuclei have become essential in pathology due to cellular nuclei counting and nuclear pleomorphism analysis are critical for the classification and grading of breast cancer histopathology. This work describes a methodology for automatic detection and segmentation of cellular nuclei in breast cancer histopathology images obtained from the BreakHis database, the Standford tissue microarray database, and the Breast Cancer Cell Segmentation database. The proposed scheme is based on the characterization of Hematoxylin and Eosin (H&E) staining, size, and shape features. In addition, we use the information obtained from morphological transformations and adaptive intensity adjustments to detect and separate each cell nucleus detected in the image. The segmentation was carried out by testing the proposed methodology in a histological breast cancer database that provides the associated groundtruth segmentation. Subsequently, the Sørensen-Dice similarity coefficient was calculated to analyze the suitability of the results.Clinical relevance- In this work, the detection and segmentation of cell nuclei in breast cancer histological images are carried out automatically. The method can identify cell nuclei regardless of variations in the level of staining and image magnification. Moreover, a granulometric analysis of the components allows identifying cell clumps and segment them into individual cell nuclei. Improved identification of cell nuclei under different image conditions was demonstrated to reach a sensitivity average of 0.76 ± 0.12. The results provide a base for further and complex processes such as cell counting, feature analysis, and nuclear pleomorphism, which are relevant tasks in the evaluation and diagnostic performed by the expert pathologist.

Detection and classification of nuclei in histopathology images is an important step in the research of understanding tumor microenvironment and evaluating cancer progression and prognosis. The task is challenging due to imaging factors such as varying cell morphologies, batch-to-batch variations in staining, and sample preparation. We present a two-stage deep learning pipeline that combines a Fully Convolutional Regression Network (FCRN) that performs nuclei localization with a Convolution Neural Network (CNN) that performs nuclei classification. Instead of using hand-crafted features, the system learns the visual features needed for detection and classification of nuclei making the process robust to the aforementioned variations. The performance of the proposed system has been quantitatively evaluated on images of hematoxylin and eosin (H&E) stained colon cancer tissues and compared to the previous studies using the same data set. The proposed deep learning system produces promising results for detection and classification of nuclei in histopathology images.

Pathological examinations play a critical role in the diagnosis process. Pathologists analyze biopsies to make a diagnosis. However, detection of nuclei in histopathology images is a costly procedure in terms of time. Because of the complexity of histopathology images, different observers might reach different conclusions. Recently, automatic digital pathology, which is faster therefore beneficial for patients and pathologists, draw many attention for research and clinical practice. In comparison to manual image analysis, computerized methods are not affected by the inter-observer variations. In this paper, we automated the nuclei detection process using deep convolutional neural networks (CNN) and simulated annealing to find center coordinates of nuclei in hematoxylin and eosin (H&E) stained histopathology images of colorectal adenocarcinoma.

Nuclei segmentation is a fundamental upstream task of digital pathology image analysis. Existing nuclei segmentation methods usually require pixel-level labeled images from experienced pathologists. In this paper, we proposed an innovative data augmentation workflow for histopathology images: a) generates a set of initial central points randomly with existing human-annotated histopathology image datasets; b) generates nuclei segmentation masks based on the generated centroid points of step a); c) generates Haematoxylin and Eosin (H&E)-stained histopathology images corresponding to the generated nuclei masks. In addition, we proposed a deep attention feature fusion generative adversarial network (DAFF -GAN) to improve the image quality and the photorealism of the generated image. We conducted extensive experiments on several existing nuclei segmentation methods, comparing using raw data with the augmented data by our strategy. Extensive experiments proved the effectiveness of our proposed strategy.

Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively.

Histopathological image analysis plays a crucial role in diagnosing cancer by examining tissue specimens under a microscope. Traditional manual methods are limited by their inability to scale efficiently for large datasets. With the rise of digital pathology, automated image analysis has become essential for handling large volumes of tissue samples, enabling faster, more accurate cancer detection. Nuclei segmentation and tissue classification are fundamental tasks, but existing methods struggle with complex tissue structures, particularly overlapping or clumped nuclei. The primary challenge in histopathology image analysis is accurately segmenting individual nuclei, especially in cases where nuclei are clumped or overlapping. Existing segmentation techniques like thresholding or conventional deep learning models often fail to address these challenges, leading to poor segmentation quality. Consequently, this affects the accuracy of classification models and, ultimately, the reliability of cancer diagnosis. This study proposes a novel approach that combines deep learning-based segmentation with improved watershed algorithms to enhance the accuracy of nuclei detection and separation. The method begins with a convolutional neural network (CNN) model for initial blob detection, followed by an improved watershed segmentation to separate clumped nuclei. A refined deep learning model (U-Net or Mask R-CNN) is then employed to further improve the segmentation results. Morphological and statistical features are extracted from the segmented nuclei, which are subsequently used in a machine learning classifier (e.g., Random Forest, SVM) to classify tissue patches as tumour or non-tumour. The proposed method is evaluated on a dataset of annotated histopathology images. The proposed method outperformed existing techniques in both training and test phases. On the training set, it achieved an accuracy of 96.2%, precision of 94.7%, recall of 97.1%, and F-measure of 95.9%. On the test set, the accuracy was 92.4%, precision was 90.3%, recall was 94.1%, and F-measure was 92.2%. Compared to traditional methods (e.g., thresholding + SVM), the proposed method demonstrated superior performance, particularly in handling clumped nuclei and producing more reliable classifications.

Cell nucleus segmentation is a fundamental task in biomedical image analysis. Generating realistic cell nucleus data with ground truth masks can help tackle difficulties such as insufficient training data for deep learning models and the need to deal with cases (e.g., tightly clumped nuclei). Known nucleus generation methods generated individual nucleus masks from parametric models or based on direct transformations of real masks. It is difficult for these methods to capture and simulate the distributions of real nuclei and interactions among hard nuclei. In this paper, we propose a new three-stage coarse-to-fine nucleus generation method for 2D and 3D nucleus segmentation. The first stage simulates the positions and sizes of nuclei; the second stage simulates the shapes of nuclei and interactions among clumped nuclei; the third stage simulates the textures of nuclei. We evaluate our method on 2D and 3D cell nucleus image datasets. Experimental results show that our new nucleus generation method considerably helps improve cell nucleus segmentation performance and outperforms known nucleus generation methods for nucleus segmentation with a small amount of training data.

The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human intervention. In this paper, we propose a deep convolution neural network, named as deep U-Net model, for the segmentation of the cell nucleus, a critical functional unit that determines the function and structure of the body. The nucleus contains all kinds of DNA, RNA, chromosomes, and genes governing all life activities, and its disorder may lead to different types of diseases such as cancer, heart disease, diabetes, Alzheimer’s, etc. If the nucleus structure is known correctly, diseases due to nucleus disorder may be detected early. It may also reduce the drug discovery time if the shape and size of the nucleus are known. We evaluate the performance of the proposed models on the nucleus segmentation data set used by the Data Science Bowl 2018 competition hosted by Kaggle. We compare its performance with that of the U-Net, Attention U-Net, R2U-Net, Attention R2U-Net, and both versions of the U-Net++ with and without supervision, in terms of loss, dice coefficient, dice loss, intersection over union, and accuracy. Our model performs better than the existing models.

A nuclei segmentation in the histopathological images is a challenging problem, specifically in the images stained with the hematoxylin and eosin dye. To alleviate this, a new whale optimization algorithm based fuzzy clustering method has been presented in this work for nuclei segmentation in the histopathological images. In the proposed method, the whale optimization algorithm is applied to find the best fuzzy clusters based on the objective function defined as compactness. The evaluation of the introduced method is conducted on the publicly available histopathological image dataset, namely H&E stained ER+ breast cancer images. The experimental results evince that the proposed approach acquires comparatively better accuracy of segmentation than the standard clustering based image segmentation methods in terms of both the qualitative and quantitative measurements. Therefore, it can be stated that the introduced method is an efficacious nuclei segmentation method for histopathological images.

Nucleus segmentation in histopathology images holds great clinical value for disease analysis. With the advancements in deep learning and continuous iteration of devices, existing nucleus segmentation algorithms can achieve satisfactory performance. However, these fully supervised nucleus segmentation methods require pixel-level annotation information, which requires a significant amount of time and effort. To ease this burden of annotations, we propose an envelope enhancement segmentation Network (EESNet) for weak supervison nucleus segmentation. Specifically, we first employ a Voronoi diagram and adaptive k-means clustering algorithm to derive two kinds of informative pixel-level pseudo labels,which are used for pixel-wise segmentation. Furthermore, we introduce an envelope enhancement network to provide rich envelope structure information to the segmentation network, which helps mitigate the problem of inaccurate boundary segmentation caused by point annotations. Finally, our network is verified on a mainstream nucleus segmentation datasets (consep) and achieves satisfactory performance.

Manufacturing process of Lithium-ion battery electrode has a direct impact on the resulting practical properties of the cell such as durability, safety and overall performance. In this scenario, together with experimental efforts to understand the correlation between manufacturing process parameters and final overall electrode performance, computational tools have shown the potential to produce insights on the manufacturing properties interdependencies. The ARTISTIC initiative [1] pioneered the development of a series of computational 3D-resolved physics-based models describing each step of the manufacturing process following a sequential pattern, going from the slurry phase, through the drying phase, to produce a calendered electrode. Such physics-based models have the capability to predict how manufacturing parameters impact the electrode microstructure. At the end of each phase the output microstructure is carefully validated with experimental data, assuring that the model is in good agreement with real properties. For the calendering phase, the Discrete Element Method (DEM) is used for the model simulation.One of the main limitations of the DEM simulations is their high computational cost, preventing their direct application in electrode optimization loops. In this matter, and given the great amount of already validated data that was produced in our previous works by using the ARTISTIC model, we take one step forward in the development of a new generation model employing machine learning (ML) techniques to accelerate the physics-based simulations but keeping their accuracy. In this presentation we report a novel time-dependent deep learning (DL) model of the battery electrodes manufacturing process. The DL model is demonstrated for calendering of nickel manganese cobalt (NMC111) electrodes, and trained with time-series data arising from physics-based DEM simulations coming from the ARTISTIC physics-based modeling pipeline [2]. The innovative DL model presented in this work can predict an electrode microstructure evolution over time at a given compression degree during calendering and provide the associated final relaxed electrode microstructure. Here we used the formulation of 96% AM and 4% CBD as a proof of concept.The DL model predictions are validated by comparing data evaluation metrics (e.g., mean square error (MSE) and R2 score) and electrode functional metrics (contact surface area, porosity, diffusivity, and tortuosity factor), showing very good accuracy with respect to the DEM simulations (all the properties are above 90% accurate when compared to target data). Regarding transport properties and given the high accuracy of the DL model to predict tortuosity factor and porosity values we can conclude that the model correctly predicts the effect of the calendering on the transport properties since tortuosity factor and porosity has a high impact on the effective transport properties of Li-ions in the electrolyte. The DL model can remarkably capture the elastic recovery of the electrode upon compression (spring-back phenomenon) and the main 3D electrode microstructure features without using the functional descriptors during its training. In terms of computational cost, the DL model performs a timestep in 15 seconds (wall time) while the DEM model performs an equivalent time step in ∼47 minutes (wall time). Given the DL model significant lower computational cost than the DEM simulations, it paves the way toward quasi-real-time optimization loops of the 3D electrode architecture predicting the calendering conditions to adopt in order to obtain the desired electrode performance. Moreover, we consider that our model has tremendous potential to grow in several aspects such as accuracy and predictability. Therefore, our incoming efforts will be focused on continuing the development of the model and testing it for other formulations and in other manufacturing stages.[1] ARTISTIC Project website. https://www.erc-artistic.eu.[2] D. E. Galvez-Aranda, T. L. Dinh, U. Vijay, F. M. Zanotto, A. A. Franco, Time-Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction. Adv. Energy Mater. 2024, 2400376. https://doi.org/10.1002/aenm.202400376 Figure 1