Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI

• URL: http://arxiv.org/abs/2508.15979v2
• Date: Mon, 13 Oct 2025 20:17:13 GMT
• Title: Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI
• Abstract summary: This study presents a low-cost, lightweight, annotation-free segmentation method by introducing one-time calibration-assisted unsupervised framework across imaging modalities and image type.<n>The framework determines background via spatial standard deviation from the local mean. Uncertain pixels are resolved using fuzzy logic, cumulative squared shift of nodal intensity, statistical features, followed by post-segmentation denoising calibration.
• Score: 3.5092739016434567
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brightfield microscopy of unstained live cells is challenging due to low contrast, dynamic morphology, uneven illumination, and lack of labels. Deep learning achieved SOTA performance on stained, high-contrast images but needs large labeled datasets, expensive hardware, and fails under uneven illumination. This study presents a low-cost, lightweight, annotation-free segmentation method by introducing one-time calibration-assisted unsupervised framework adaptable across imaging modalities and image type. The framework determines background via spatial standard deviation from the local mean. Uncertain pixels are resolved using fuzzy logic, cumulative squared shift of nodal intensity, statistical features, followed by post-segmentation denoising calibration which is saved as a profile for reuse until noise pattern or object type substantially change. The program runs as a script or graphical interface for non-programmers. The method was rigorously evaluated using \textit{IoU}, \textit{F1-score}, and other metrics, with statistical significance confirmed via Wilcoxon signed-rank tests. On unstained brightfield myoblast (C2C12) images, it outperformed \textit{Cellpose 3.0} and \textit{StarDist}, improving IoU by up to 48\% (average IoU = 0.43, F1 = 0.60). In phase-contrast microscopy, it achieved a mean IoU of 0.69 and an F1-score of 0.81 on the \textit{LIVECell} dataset ($n = 3178$), with substantial expert agreement ($\kappa > 0.75$) confirming cross-modality robustness. Successful segmentation of laser-affected polymer surfaces further confirmed cross-domain robustness. By introducing the \textit{Homogeneous Image Plane} concept, this work provides a new theoretical foundation for training-free, annotation-free segmentation. The framework operates efficiently on CPU, avoids cell staining, and is practical for live-cell imaging and biomedical applications.

Related papers

• Vision-Language Models as Zero-Annotation Oracles in Histopathology [10.853300035340615]
  Foreground segmentation is the critical first step of every computational pathology pipeline.<n>We propose a coarse-to-fine approach that recasts foreground segmentation as a visual perception task.<n>Our framework provides a principled, scalable solution to a persistent infrastructure bottleneck in digital pathology.
  arXiv  Detail & Related papers  (2026-06-15T12:50:19Z)
• Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning [52.41928980786654]
  We propose PathCTM to enable token-efficient scale-space continuous reasoning for gigapixel whole slide images.<n>PathCTM formulates diagnostic inference as a dynamic sequential information pursuit.<n>It reduces the number of required image patches by 95.95% and shortens inference time by approximately 95.62%, while maintaining AUC without degradation.
  arXiv  Detail & Related papers  (2026-05-19T07:46:44Z)
• Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs [77.42033827176806]
  One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm.<n>We propose a novel Federated Model Inversion and Token Relabel framework, which trains the global model by fully exploiting all patches of synthetic images.
  arXiv  Detail & Related papers  (2026-05-11T15:49:08Z)
• A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation [17.122762119608144]
  Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift.<n>We propose textbfA3-TTA, a framework that constructs reliable pseudo-labels through anchor-guided supervision.<n>A3-TTA significantly improves average Dice scores by 10.40 to 17.68 percentage points compared to the source model.
  arXiv  Detail & Related papers  (2026-02-03T09:18:11Z)
• Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment [3.2371089062298317]
  Grad-CL is a novel source-free domain adaptation framework.<n>It adapts segmentation performance without requiring access to original source data.<n>It outperforms state-of-the-art unsupervised and source-free domain adaptation methods.
  arXiv  Detail & Related papers  (2025-09-12T10:51:46Z)
• High-Throughput Low-Cost Segmentation of Brightfield Microscopy Live Cell Images [3.175346985850522]
  This study focuses on segmenting unstained live cells imaged with bright-field microscopy.<n>We developed a low-cost CNN-based pipeline incorporating comparative analysis of frozen encoders.<n>The model was validated on a public dataset featuring diverse live cell variants.
  arXiv  Detail & Related papers  (2025-08-17T22:05:58Z)
• Foreground-aware Virtual Staining for Accurate 3D Cell Morphological Profiling [44.74519211679785]
  We introduce Spotlight, a virtual staining approach that guides the model to focus on relevant cellular structures.<n> Spotlight improves morphological representation while preserving pixel-level accuracy, resulting in virtual stains better suited for downstream tasks such as segmentation and profiling.
  arXiv  Detail & Related papers  (2025-07-07T18:11:56Z)
• How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings [106.3726679697804]
  We compare the two most common techniques for mitigating this spectral bias: Fourier feature encodings (FFE) and multigrid parametric encodings (MPE)<n>MPEs are seen as the standard for low dimensional mappings, but MPEs often outperform them and learn representations with higher resolution and finer detail.<n>We prove that MPEs improve a network's performance through the structure of their grid and not their learnable embedding.
  arXiv  Detail & Related papers  (2025-04-18T02:18:08Z)
• A Graph-Based Framework for Interpretable Whole Slide Image Analysis [86.37618055724441]
  We develop a framework that transforms whole-slide images into biologically-informed graph representations.<n>Our approach builds graph nodes from tissue regions that respect natural structures, not arbitrary grids.<n>We demonstrate strong performance on challenging cancer staging and survival prediction tasks.
  arXiv  Detail & Related papers  (2025-03-14T20:15:04Z)
• Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging [7.915123555266876]
  We propose a bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures.<n>We evaluate our methodology through experiments conducted on several widely used biomedical datasets.<n>Results show that our proposed method outperforms SOTA approaches across different levels of label availability.
  arXiv  Detail & Related papers  (2024-12-04T10:25:53Z)
• An efficient framework based on large foundation model for cervical cytopathology whole slide image screening [13.744580492120749]
  We propose an efficient framework for cervical cytopathology WSI classification using only WSI-level labels through unsupervised and weakly supervised learning. Experiments conducted on the CSD and FNAC 2019 datasets demonstrate that the proposed method enhances the performance of various MIL methods and achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2024-07-16T08:21:54Z)
• Image-level Regression for Uncertainty-aware Retinal Image Segmentation [3.7141182051230914]
  We introduce a novel Uncertainty-Aware (SAUNA) transform, which adds pixel uncertainty to the ground truth. Our results indicate that the integration of the SAUNA transform and these segmentation losses led to significant performance boosts for different segmentation models.
  arXiv  Detail & Related papers  (2024-05-27T04:17:10Z)
• Decoupled Prototype Learning for Reliable Test-Time Adaptation [50.779896759106784]
  Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. We propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation.
  arXiv  Detail & Related papers  (2024-01-15T03:33:39Z)
• Semi-Supervised End-To-End Contrastive Learning For Time Series Classification [9.46287383389573]
  Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis.<n>We propose an end-to-end model called SLOTS (Semi-supervised Learning fOr Time clasSification)
  arXiv  Detail & Related papers  (2023-10-13T04:22:21Z)
• Weakly-supervised positional contrastive learning: application to cirrhosis classification [45.63061034568991]
  Large medical imaging datasets can be cheaply annotated with low-confidence, weak labels. Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. We propose an efficient weakly-supervised positional (WSP) contrastive learning strategy.
  arXiv  Detail & Related papers  (2023-07-10T15:02:13Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection [64.65563422852568]
  We improve the challenging monocular 3D object detection problem with a general semi-supervised framework. We introduce a novel, simple, yet effective Augment and Criticize' framework that explores abundant informative samples from unlabeled data. The two new detectors, dubbed 3DSeMo_DLE and 3DSeMo_FLEX, achieve state-of-the-art results with remarkable improvements for over 3.5% AP_3D/BEV (Easy) on KITTI.
  arXiv  Detail & Related papers  (2023-03-20T16:28:15Z)
• Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification [61.656149405657246]
  Domain adaptation is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2022-09-27T14:19:00Z)
• Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images [57.42492501915773]
  In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Human fatigue plays a role in error making, making the use of deep learning appealing. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI) Our method successfully provides an adaptive early learning correction technique for object detection.
  arXiv  Detail & Related papers  (2022-08-05T18:52:20Z)
• Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
  It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
  arXiv  Detail & Related papers  (2022-05-02T16:09:17Z)
• GradViT: Gradient Inversion of Vision Transformers [83.54779732309653]
  We demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. We introduce a method, named GradViT, that optimize random noise into naturally looking images. We observe unprecedentedly high fidelity and closeness to the original (hidden) data.
  arXiv  Detail & Related papers  (2022-03-22T17:06:07Z)
• MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels [5.500466607182699]
  Semi-supervised learning is a promising paradigm to address the issue of label scarcity in medical imaging. MisMatch is a semi-supervised segmentation framework based on the consistency between paired predictions.
  arXiv  Detail & Related papers  (2021-10-23T09:22:41Z)
• ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation [99.90263375737362]
  We propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
  arXiv  Detail & Related papers  (2020-06-24T04:05:12Z)
• Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling [69.35569554213679]
  We show that the Analytic Signal phase, and its gradient have a hitherto unstudied discontinuity in $2-D $ and higher dimensions. This shortcoming can result in severe artifacts whereas the problem does not exist in $1-D $ signals. We suggest the use of Linear Symmetry phase, relying on more than one set of Gabor filters, but with a negligible computational add-on.
  arXiv  Detail & Related papers  (2020-05-16T21:17:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.