Virtual 3D H&E Staining from Phase-contrast Back-illumination Interference Tomography

• URL: http://arxiv.org/abs/2605.22000v1
• Date: Thu, 21 May 2026 04:58:09 GMT
• Title: Virtual 3D H&E Staining from Phase-contrast Back-illumination Interference Tomography
• Abstract summary: HistoBIT3D is the first voxel-wise paired BIT and fluorescence-labeled nuclei dataset.<n>We present a novel virtual staining framework that translates BIT volumes with shift-variant contrast into realistic H&E volumes.<n>Our method achieves state-of-the-art realism metrics while significantly improving 3D nuclei segmentation accuracy and boundary preservation.
• Score: 1.560016918889092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Three-dimensional (3D) histopathology of unprocessed tissues has the potential to transform disease management by enabling volumetric characterization of tissue microarchitecture and in-vivo assessment. Back-illumination Interference Tomography (BIT) is a new phase microscopy technology that provides rapid, non-destructive volumetric imaging of unprocessed tissues. However, translating BIT volumes into clinically interpretable H&E images remains challenging, particularly due to shift-variant contrast and the absence of quantitative validation benchmarks. We introduce HistoBIT3D, the first voxel-wise paired BIT and fluorescence-labeled nuclei dataset, enabling quantitative evaluation of structural preservation in unsupervised virtual staining against ground-truth nuclear distributions. Using this dataset, we present a novel virtual staining framework that translates BIT volumes with shift-variant contrast into realistic H&E volumes by leveraging bidirectional multiscale content consistency and cross-domain style reuse to enhance structural fidelity and perceptual realism. Our method achieves state-of-the-art realism metrics while significantly improving 3D nuclei segmentation accuracy and boundary preservation under zero-shot Cellpose evaluation. Together, these contributions establish a quantitatively validated, structurally faithful, and scalable pipeline for 3D virtual H&E staining, advancing the paradigm of slide-free, volumetric computational histopathology. Our data and code are available at: https://github.com/aasong113/HistoBIT3D_VirtualStaining.

Related papers

• Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers [57.54843029965778]
  Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail.<n>We propose In-Context Diffusion Transformer (IC-DiT), a layout-aware generative model that incorporates spatial layouts, textual descriptions, and visual embeddings into a unified diffusion transformer.<n>IC-DiT achieves higher fidelity, stronger spatial controllability, and better diagnostic consistency than existing methods.
  arXiv  Detail & Related papers  (2026-03-11T06:14:11Z)
• 3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification [80.43401018217367]
  3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction.<n>Its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows.<n>Existing methods rely on semantic cues extracted from pre-trained vision models to identify and suppress these distractors.<n>We propose 3DGS- HPC, a framework that circumvents these limitations by combining two complementary principles.
  arXiv  Detail & Related papers  (2026-03-08T11:16:30Z)
• The Texture-Shape Dilemma: Boundary-Safe Synthetic Generation for 3D Medical Transformers [22.195374577712133]
  ViTs have revolutionized medical image analysis, yet their data-hungry nature clashes with the scarcity and privacy constraints of clinical archives.<n>Formula-Driven Supervised Learning has emerged as a promising solution to this bottleneck, synthesizing infinite annotated samples from mathematical formulas without utilizing real patient data.<n>Existing F paradigms rely on simple geometric with homogeneous intensities, creating a substantial gap by neglecting tissue textures and noise patterns inherent in modalities like CT and MRI.<n>In this paper, we identify a critical conflict boundary aliasing: when high-frequency synthetic textures are naively added, they corrupt the image gradient signals necessary for learning structural boundaries, causing
  arXiv  Detail & Related papers  (2026-03-01T08:27:45Z)
• 3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology [0.9562447539143751]
  HoloTea is a 3D-aware flow-matching framework that imputes spot-level gene expression from H&E while explicitly using information from adjacent sections.<n>A global attention block introduces 3D H&E scaling linearly with the number of spots in the slide, enabling training and inference on large 3D ST datasets.
  arXiv  Detail & Related papers  (2025-11-18T16:08:24Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• Virtual staining for 3D X-ray histology of bone implants [2.4922739987890767]
  Three-dimensional X-ray histology techniques offer a non-invasive alternative to conventional 2D histology.<n>Deep learning-based virtual staining has demonstrated utility in simulating stained appearances from label-free optical images.<n>This work introduces virtual staining to 3D X-ray imaging and offers a scalable route for chemically informative, label-free tissue characterisation.
  arXiv  Detail & Related papers  (2025-09-11T08:14:31Z)
• Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics [0.433313790597694]
  Vision Transformer Digital Twin Surrogate Network (VT-DTSN) is a deep learning framework for predictive modeling of 3D+T imaging data from biological tissue.<n>VT-DTSN learns to reconstruct high-fidelity, time-resolved dynamics of a Drosophila midgut.
  arXiv  Detail & Related papers  (2025-08-21T16:24:24Z)
• Reference-Guided Diffusion Inpainting For Multimodal Counterfactual Generation [55.2480439325792]
  Safety-critical applications, such as autonomous driving and medical image analysis, require extensive multimodal data for rigorous testing.<n>This work introduces two novel methods for synthetic data generation in autonomous driving and medical image analysis, namely MObI and AnydoorMed, respectively.
  arXiv  Detail & Related papers  (2025-07-30T19:43:47Z)
• A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging [36.95712533471744]
  We introduce a general virtual staining framework that is adaptable to various conditions.<n>We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities.
  arXiv  Detail & Related papers  (2025-01-07T07:45:21Z)
• T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
  Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-11-29T07:45:24Z)
• Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation [66.3814684757376]
  This work presents Zero123-6D, the first work to demonstrate the utility of Diffusion Model-based novel-view-synthesizers in enhancing RGB 6D pose estimation at category-level. The outlined method shows reduction in data requirements, removal of the necessity of depth information in zero-shot category-level 6D pose estimation task, and increased performance, quantitatively demonstrated through experiments on the CO3D dataset.
  arXiv  Detail & Related papers  (2024-03-21T10:38:18Z)

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.