Search Results (84)

Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei, such as lymphomas, due to the loss of spatial depth information. Here, we tested if AI-based 3D nuclear segmentation can improve the accuracy, reproducibility, and diagnostic reliability of FISH reading in critical situations. Materials and Methods: Formalin-fixed follicular lymphoma sections were FISH-labeled for BCL2 gene rearrangements and digitally scanned in multilayer Z-stacks. The analytic performance in nuclear segmentation of the adaptive thresholding-based FISHQuant, and the freely accessible AI-based NucleAIzer, StarDist, and Cellpose algorithms, were compared to the eye control-based traditional FISH testing, primarily focusing on nuclear segmentation. Results: We revealed that the Cellpose algorithm showed limited sensitivity to low-intensity signals and the adaptive thresholding 2D segmentation, and FISHQuant struggled to resolve densely packed nuclei, occasionally underestimating their counts. In contrast, 3D segmentation across focal planes significantly improved the nuclear separation and signal localization. AI-driven 3D models, especially NucleAIzer and StarDist, showed improved precision, lower variance (VP/VS ≈ 0.96), and improved gene spot correlation (r > 0.82) across multiple focal planes. The similar average number of gene spots per cell nuclei in the AI-based analyses as the eye control counting, despite the elevated number of cell nuclei found with AI, validated the AI nuclear segmentation results. Conclusions: Inaccurate segmentation limits automated diagnostic FISH signal evaluation. Deep learning 3D approaches, particularly NucleAIzer and StarDist, may overcome thresholding and 2D constraints and improve the consistency of nuclear detection, resulting in better classification of pathogenetic gene aberrations with automated workflows in digital pathology. Full article

Accurate segmentation of nuclei and cytoplasm in cervical cytology images plays a pivotal role in characterizing cellular morphology. The primary challenge is to precisely delineate boundaries within densely clustered cells, which is complicated by low-contrast edges and irregular morphologies. This paper introduces a novel framework combining MaskDino architecture with Hausdorff distance loss, enhanced by a two-phase training strategy. The method begins by employing MaskDino for precise nucleus segmentation. Building on this foundation, the framework then enhances cytoplasmic boundary detection in cellular clusters by incorporating a Hausdorff distance loss, with weight transfer initialization ensuring feature consistency across tasks.. The symmetry between the nucleus and cytoplasm servers as a key morphological indicator for cell assessment, and our method provides a reliable basis for such analysis. Extensive experiments demonstrate that our method achieves state-of-the-art cytoplasm segmentation results on the ISBI2014 dataset, with absolute improvements of 2.9% in DSC, 1.6% in TPRp and 2.0% in FNRo. The performance of nucleus segmentation is better than the average level. These results validate the proposed framework’s effectiveness for improving cervical cancer screening through robust cellular segmentation. Full article

Cell nucleus segmentation in microscopy images is an initial step in the quantitative analysis of imaging data, which is crucial for diverse biological and biomedical applications. While traditional machine learning methodologies have demonstrated limitations, recent advances in U-Net models have yielded promising improvements. However, it is noteworthy that these models perform well on balanced datasets, where the ratio of background to foreground pixels is equal. Within the realm of microscopy image segmentation, state-of-the-art models often encounter challenges in accurately predicting small foreground entities such as nuclei. Moreover, the majority of these models exhibit large parameter sizes, predisposing them to overfitting issues. To overcome these challenges, this study introduces a novel architecture, called mA-UNet, designed to excel in predicting small foreground elements. Additionally, a data preprocessing strategy inspired by road segmentation approaches is employed to address dataset imbalance issues. The experimental results show that the MIoU score attained by the mA-UNet model stands at $95.50\%$, surpassing the nearest competitor, UNet++, on the 2018 Data Science Bowl dataset. Ultimately, our proposed methodology surpasses all other state-of-the-art models in terms of both quantitative and qualitative evaluations. The mA-UNet model is also implemented in VHDL on the Zynq UltraScale+ FPGA, demonstrating its ability to perform complex computations with minimal hardware resources, as well as its efficiency and scalability on advanced FPGA platforms. Full article

Accurate computer-aided cell detection in immunohistochemistry images of different tissues is essential for advancing digital pathology and enabling large-scale quantitative analysis. This paper presents a comprehensive comparison of six unsupervised segmentation methods against two supervised deep learning approaches for cell detection in immunohistochemistry images. The unsupervised methods are based on the continuity and similarity image properties, using techniques like clustering, active contours, graph cuts, superpixels, or edge detectors. The supervised techniques include the YOLO deep learning neural network and the U-Net architecture with heatmap-based localization for precise cell detection. All these methods were evaluated using leave-one-image-out cross-validation on the publicly available OIADB dataset, containing 40 oral tissue IHC images with over 40,000 manually annotated cells, assessed using precision, recall, and $F_1$-score metrics. The U-Net model achieved the highest performance for cell nuclei detection, an $F_1$-score of 75.3%, followed by YOLO with $F_1$ = 74.0%, while the unsupervised OralImmunoAnalyser algorithm achieved only $F_1$ = 46.4%. Although the two former are the best solutions for automatic pathological assessment in clinical environments, the latter could be useful for small research units without big computational resources. Full article

Background and Objective: Cervical cancer remains one of the leading causes of death among women worldwide, particularly in regions with limited access to early screening. Pap smear screening is the primary tool for early detection, but manual interpretation is labor-intensive, subjective, and prone to inconsistency and misdiagnosis. Accurate segmentation of cervical cell nuclei is essential for automated analysis but is often hampered by overlapping cells, poor contrast, and staining variability. This research aims to develop an improved algorithm for accurate cervical nucleus segmentation to support automated Pap smear analysis. Method: The proposed method involves a combination of adaptive gamma correction for contrast enhancement, followed by Otsu thresholding for segmentation. Post-processing is performed using adaptive morphological operations to refine the results. The system is evaluated using standard image quality assessment metrics and validated against ground truth annotations. Result: The results show a significant improvement in segmentation performance over conventional methods. The proposed algorithm achieved a Precision of 0.9965, an F-measure of 97.29%, and an Accuracy of 98.39%. The PSNR value of 16.62 indicates enhanced image clarity after preprocessing. The method also improved sensitivity, leading to better identification of nuclei boundaries. Advanced preprocessing techniques, including edge-preserving filters and multi-Otsu thresholding, contributed to more accurate cell separation. The segmentation method proved effective across varying cell overlaps and staining conditions. Comparative evaluations with traditional clustering methods confirmed its superior performance. Conclusions: The proposed algorithm delivers robust and accurate segmentation of cervical cell nuclei, addressing common challenges in Pap smear image analysis. It provides a consistent framework for automated screening tools. This work enhances diagnostic reliability in cervical cancer screening and offers a foundation for broader applications in medical image analysis. Full article

Precise segmentation of cell nuclei in pathological images is the foundation of cancer diagnosis and quantitative analysis, but blurred boundaries, scale variability, and staining differences have long constrained its reliability. To address this, this paper proposes AL-Net—an adaptive learning network that breaks through these bottlenecks through three innovative mechanisms: First, it integrates dilated convolutions with attention-guided skip connections to dynamically integrate multi-scale contextual information, adapting to variations in cell nucleus morphology and size. Second, it employs self-scheduling loss optimization: during the initial training phase, it focuses on region segmentation (Dice loss) and later switches to a boundary refinement stage, introducing gradient manifold constraints to sharpen edge localization. Finally, it designs an adaptive optimizer strategy, leveraging symbolic exploration (Lion) to accelerate convergence, and switches to gradient fine-tuning after reaching a dynamic threshold to stabilize parameters. On the 2018 Data Science Bowl dataset, AL-Net achieved state-of-the-art performance (Dice coefficient 92.96%, IoU 86.86%), reducing boundary error by 15% compared to U-Net/DeepLab; in cross-domain testing (ETIS/ColonDB polyp segmentation), it demonstrated over 80% improvement in generalization performance. AL-Net establishes a new adaptive learning paradigm for computational pathology, significantly enhancing diagnostic reliability. Full article

Computer-aided systems can assist doctors in detecting cancer at an early stage using medical image analysis. In estrogen receptor immunohistochemistry (ER-IHC)-stained whole-slide images, automated cell identification and segmentation are helpful in the prediction scoring of hormone receptor status, which aids pathologists in determining whether to recommend hormonal therapy or other therapies for a patient. Accurate scoring can be achieved with accurate segmentation and classification of the nuclei. This paper presents two main objectives: first is to identify the top three models for this classification task and establish an ensemble model, all using 10-fold cross-validation strategy; second is to detect recurring misclassifications within the dataset to identify “misclassified nuclei” or “incorrectly labeled nuclei” for the nuclei class ground truth. The classification task is carried out using 32 pre-trained deep learning models from Keras Applications, focusing on their effectiveness in classifying negative, weak, moderate, and strong nuclei in the ER-IHC histopathology images. An ensemble learning with logistic regression approach is employed for the three best models. The analysis reveals that the top three performing models are EfficientNetB0, EfficientNetV2B2, and EfficientNetB4 with an accuracy of 94.37%, 94.36%, and 94.29%, respectively, and the ensemble model’s accuracy is 95%. We also developed a web-based platform for the pathologists to rectify the “faulty-class” nuclei in the dataset. The complete flow of this work can benefit the field of medical image analysis especially when dealing with intra-observer variability with a large number of images for ground truth validation. Full article

Histopathological images play a crucial role in diagnosing skin cancer. However, due to the very large size of digital histopathological images (typically in the order of billion pixels), manual image analysis is tedious and time-consuming. Therefore, there has been significant interest in developing Artificial Intelligence (AI)-enabled computer-aided diagnosis (CAD) techniques for skin cancer detection. Due to the diversity of uncertain cell boundaries, automated nuclei segmentation of histopathological images remains challenging. Automating the identification of abnormal cell nuclei and analyzing their distribution across multiple tissue sections can significantly expedite comprehensive diagnostic assessments. In this paper, a deep neural network (DNN)-based technique is proposed to segment nuclei and detect melanoma in histopathological images. To achieve a robust performance, a test image is first augmented by various geometric operations. The augmented images are then passed through the DNN and the individual outputs are combined to obtain the final nuclei-segmented image. A morphological technique is then applied on the nuclei-segmented image to detect the melanoma region in the image. Experimental results show that the proposed technique can achieve a Dice score of 91.61% and 87.9% for nuclei segmentation and melanoma detection, respectively. Full article

Segmenting cell nuclei in histopathological images is an extremely important process for computational pathology, affecting not only the accuracy of a disease diagnosis but also the analysis of biomarkers and the assessment of cells performed on a large scale. Although many deep learning models can take out global and local features, it is still difficult to find a good balance between semantic context and fine boundary precision, especially when nuclei are overlapping or have changed shapes. In this paper, we put forward a novel deep learning model named Dual-Stream HyperFusionNet (DS-HFN), which is capable of explicitly representing the global contextual and boundary-sensitive features for the robust nuclei segmentation task by first decoupling and then fusing them. The dual-stream encoder in DS-HFN can simultaneously acquire the semantic and edge-focused features, which can be later combined with the help of the attention-driven HyperFeature Embedding Module (HFEM). Additionally, the dual-decoder concept, together with the Gradient-Aligned Loss Function, facilitates structural precision by making the segmentation gradients that are predicted consistent with the ground-truth contours. On various benchmark datasets like TNBC and MoNuSeg, DS-HFN not only achieves better results than other 30 state-of-the-art models in all evaluation metrics but also is less computationally expensive. These findings indicate that DS-HFN provides a capability for accurate nuclei segmentation, which is essential for clinical diagnosis and biomarker analysis, across a wide range of tissues in digital pathology. Full article

Background/Objectives: Diffuse Large B-Cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and demands precise segmentation and classification of nuclei for effective diagnosis and disease severity assessment. This study aims to evaluate the performance of HoVerNet, a deep learning model, for nuclei segmentation and classification in CMYC-stained whole slide images and to assess its integration into a user-friendly diagnostic tool. Methods: A dataset of 122 CMYC-stained whole slide images (WSIs) was used. Pre-processing steps, including stain normalization and patch extraction, were applied to improve input consistency. HoVerNet, a multi-branch neural network, was used for both nuclei segmentation and classification, particularly focusing on its ability to manage overlapping nuclei and complex morphological variations. Model performance was validated using metrics such as accuracy, precision, recall, and F1 score. Additionally, a graphic user interface (GUI) was developed to incorporate automated segmentation, cell counting, and severity assessment functionalities. Results: HoVerNet achieved a validation accuracy of 82.5%, with a precision of 85.3%, recall of 82.6%, and an F1 score of 83.9%. The model showed powerful performance in differentiating overlapping and morphologically complex nuclei. The developed GUI enabled real-time visualization and diagnostic support, enhancing the efficiency and usability of DLBCL histopathological analysis. Conclusions: HoVerNet, combined with an integrated GUI, presents a promising approach for streamlining DLBCL diagnostics through accurate segmentation and real-time visualization. Future work will focus on incorporating Vision Transformers and additional staining protocols to improve generalizability and clinical utility. Full article

Retinal degeneration is associated with dietary factors and environmental light exposure. This study investigated the effects of a high-fructose high-fat (HFHF) diet on susceptibility to blue light (BL)-induced retinal damage. Male ICR mice were randomized into three groups: control, BL alone, and BL plus HFHF diet (BL + HFHF). The BL + HFHF group consumed the HFHF diet for 40 weeks, followed by 8 weeks of low-intensity BL exposure (465 nm, 37.7 lux, 0.8 μW/cm²) for 6 h daily. The BL group underwent the same BL exposure while kept on a standard diet. Histopathological analysis showed that, under BL exposure, the HFHF diet significantly reduced the number of photoreceptor nuclei and the thickness of the outer nuclear layer and inner/outer segments compared to the BL group (p < 0.05). While BL exposure alone caused oxidative DNA damage, rhodopsin loss, and Müller cell activation, the combination with an HFHF diet significantly amplified the oxidative DNA damage and Müller cell activation. Moreover, the HFHF diet increased blood–retinal barrier permeability and triggered apoptosis under BL exposure. Mechanistically, the BL + HFHF group exhibited increased retinal advanced glycated end product (AGE) deposition, accompanied by the activation of the receptor for AGE (RAGE), NFκB, and the NLRP3 inflammasome-dependent IL-1β pathway. In conclusion, this study underscores that unhealthy dietary factors, particularly those high in fructose and fat, may intensify the hazard of BL and adversely impact visual health. Full article

Artificial intelligence (AI) is becoming an integral part of pathological assessment and diagnostic procedures in modern pathology. As most prostate cancers (PCa) arise from glandular epithelial tissue, an AI-based methodology has been developed to recognize glandular epithelial nuclei in prostate biopsy tissue. An integrated machine-learning network, named GlandNet, was developed to correctly recognize the epithelial cells within prostate glands using cell-centric patches selected from the core biopsy specimens. Feulgen-Thionin (a DNA stoichiometric label) was used to stain biopsy sections (4–7 µm in thickness) from 82 active surveillance patients diagnosed with PCa. Images of these sections were human-annotated, and the resultant dataset consisted of 1,264,772 segmented, cell-centric nuclei patches, of which 449,879 were centered on epithelial gland nuclei from 110 needle biopsies (training set: n = 66; validation set: n = 22; and test set: n = 22). The training of GlandNet used semi-supervised machine-learning knowledge of the training and validation cohorts and integrated both human and AI predictions to enhance its performance on the test cohort. The performance was evaluated against a consensus deliberation from three observers. The GlandNet demonstrated an average accuracy, sensitivity, specificity, and F1-score of 94.1%, 95.7%, 87.8%, and 95.2%, respectively, when tested on the 20,735 glandular cells found in the three needle biopsies with the visually best consensus predictions. Conversely, the average accuracy, sensitivity, specificity, and F1-score were 90.9%, 86.4%, 94.0%, and 89.7% when assessed on 57,217 cells found in the three needle biopsies with the visually worst consensus predictions. GlandNet is a first-generation AI with an excellent ability to differentiate between epithelial and stromal nuclei in core biopsies from patients with early prostate cancer. Full article

Background: Nuclei segmentation is the first stage of automated microscopic image analysis. The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. Nucleus segmentation remains a challenging task despite significant advancements in automated methods. Traditional techniques, such as Otsu thresholding and watershed approaches, are ineffective in challenging scenarios. However, deep learning-based methods exhibit remarkable results across various biological imaging modalities, including computational pathology. Methods: This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). Handcrafted features extract attributes like the shape, texture, and intensity of nuclei and are meticulously developed based on specialized knowledge. Conversely, CNN-based features are automatically acquired representations that identify complex patterns in nuclei images. To assess how effectively these techniques segment cell nuclei, their performance is evaluated. Results: Experimental results show that Logistic Regression based on CNN-derived features outperforms the other techniques, achieving an accuracy of 96.90%, a Dice coefficient of 74.24, and a Jaccard coefficient of 55.61. In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. Conclusions: The conclusions suggest that leveraging CNN-based features in conjunction with Logistic Regression significantly enhances the accuracy of cell nuclei segmentation in pathological images. This approach holds promise for refining computer-aided pathology workflows, potentially leading to more reliable and earlier disease diagnoses. Full article

Cell nuclei instance segmentation plays a critical role in pathological image analysis. In recent years, fully supervised methods for cell nuclei instance segmentation have achieved significant results. However, in practical medical image processing, annotating dense cell nuclei at the instance level is often costly and time-consuming, making it challenging to acquire large-scale labeled datasets. This challenge has motivated researchers to explore ways to further enhance segmentation performance under limited labeling conditions. To address this issue, this paper proposes a network based on category-adaptive sampling and attention mechanisms for semi-supervised nuclei instance segmentation. Specifically, we design a category-adaptive sampling method that forces the model to focus on rare categories and dynamically adapt to different data distributions. By dynamically adjusting the sampling strategy, the balance of samples across different cell types is improved. Additionally, we propose a strong–weak contrast consistency method that significantly expands the perturbation space. Strong perturbations enhance the model’s ability to discriminate key nuclei features, while weak perturbations improve its robustness against noise and interference. Furthermore, we introduce a region-adaptive attention mechanism that dynamically assigns higher weights to key regions, guiding the model to prioritize learning discriminative features in challenging areas such as blurred or ambiguous cell boundaries. This improves the morphological accuracy of the segmentation masks. Our method effectively leverages the potential information in unlabeled data, thereby reducing reliance on large-scale, high-quality labeled datasets. Experimental results on public datasets demonstrate the effectiveness of our approach. Full article

Background/Objectives: Accurate and efficient segmentation of cell nuclei in biomedical images is critical for a wide range of clinical and research applications, including cancer diagnostics, histopathological analysis, and therapeutic monitoring. Although U-Net and its variants have achieved notable success in medical image segmentation, challenges persist in balancing segmentation accuracy with computational efficiency, especially when dealing with large-scale datasets and resource-limited clinical settings. This study aims to develop a lightweight and scalable U-Net-based architecture that enhances segmentation performance while substantially reducing computational overhead. Methods: We propose a novel evolving U-Net architecture that integrates multi-scale feature extraction, depthwise separable convolutions, residual connections, and attention mechanisms to improve segmentation robustness across diverse imaging conditions. Additionally, we incorporate channel reduction and expansion strategies inspired by ShuffleNet to minimize model parameters without sacrificing precision. The model performance was extensively validated using the 2018 Data Science Bowl dataset. Results: Experimental evaluation demonstrates that the proposed model achieves a Dice Similarity Coefficient (DSC) of 0.95 and an accuracy of 0.94, surpassing state-of-the-art benchmarks. The model effectively delineates complex and overlapping nuclei structures with high fidelity, while maintaining computational efficiency suitable for real-time applications. Conclusions: The proposed lightweight U-Net variant offers a scalable and adaptable solution for biomedical image segmentation tasks. Its strong performance in both accuracy and efficiency highlights its potential for deployment in clinical diagnostics and large-scale biological research, paving the way for real-time and resource-conscious imaging solutions. Full article