From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review

• URL: http://arxiv.org/abs/2502.19123v1
• Date: Wed, 26 Feb 2025 13:24:16 GMT
• Title: From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review
• Authors: Behnaz Elhaminia, Abdullah Alsalemi, Esha Nasir, Mostafa Jahanifar, Ruqayya Awan, Lawrence S. Young, Nasir M. Rajpoot, Fayyaz Minhas, Shan E Ahmed Raza,
• Abstract summary: Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology.<n>It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample.<n>The goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME.
• Score: 7.441179174680556
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artefacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasising their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.

Related papers

• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis [81.19923502845441]
  We develop a graph-based framework that constructs WSI graph representations. We build tissue representations (nodes) that follow biological boundaries rather than arbitrary patches. In our method's final step, we solve the diagnostic task through a graph attention network.
  arXiv  Detail & Related papers  (2025-03-14T20:15:04Z)
• Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
  Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner.<n>Method achieves state-of-the-art performance on both public and in-house datasets.
  arXiv  Detail & Related papers  (2024-12-10T10:32:09Z)
• U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities [0.0]
  Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have transformed medical image segmentation (MIS)<n>These models enable efficient, precise pixel-wise classification across various imaging modalities.<n>This review explores various medical imaging techniques, examines the U-Net architectures and their adaptations, and discusses their application across different modalities.
  arXiv  Detail & Related papers  (2024-12-03T08:11:06Z)
• Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits [0.7968706282619793]
  Multi multiplexed imaging allows for simultaneous visualization of multiple biomarkers in a single section. Data from multiplexed imaging requires sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. PathML is an AI-powered platform that streamlines image analysis, making complex interpretation accessible for clinical and research settings.
  arXiv  Detail & Related papers  (2024-11-01T18:02:41Z)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework. Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis [9.615399811006034]
  HistoGym aims to foster whole slide image diagnosis by mimicking the real-life processes of doctors. We offer various scenarios for different organs and cancers, including both WSI-based and selected region-based scenarios.
  arXiv  Detail & Related papers  (2024-08-16T17:19:07Z)
• Scribble-Based Interactive Segmentation of Medical Hyperspectral Images [4.675955891956077]
  This work introduces a scribble-based interactive segmentation framework for medical hyperspectral images. The proposed method utilizes deep learning for feature extraction and a geodesic distance map generated from user-provided scribbles.
  arXiv  Detail & Related papers  (2024-08-05T12:33:07Z)
• Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer [4.672688418357066]
  We propose a novel Transformer Diffusion (DTS) model for robust segmentation in the presence of noise. Our model, which analyzes the morphological representation of images, shows better results than the previous models in various medical imaging modalities.
  arXiv  Detail & Related papers  (2024-08-01T07:35:54Z)
• Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training [99.2891802841936]
  We introduce the Med-ST framework for fine-grained spatial and temporal modeling. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR)
  arXiv  Detail & Related papers  (2024-05-30T03:15:09Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.