Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images

• URL: http://arxiv.org/abs/2502.13974v1
• Date: Sat, 08 Feb 2025 14:03:02 GMT
• Title: Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
• Authors: Eduard Chelebian, Pratiti Dasgupta, Zainalabedin Samadi, Carolina Wählby, Amjad Askary,
• Abstract summary: SEFI (SEgmentation-Free Integration) is a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data.<n>We demonstrate SEFI on spatially resolved gene expression profiles of the developing retina, acquired using multiplexed single molecule Fluorescence In Situ Hybridization (smFISH)
• Score: 1.2200074914789645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces SEFI (SEgmentation-Free Integration), a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data. Cell segmentation poses a significant challenge in the analysis of spatial transcriptomics data, as tissue-specific structural complexities and densely packed cells in certain regions make it difficult to develop a universal approach. SEFI addresses this by utilizing self-supervised learning to extract morphological features from fluorescent nuclear staining images, enhancing the clustering of gene expression data without requiring segmentation. We demonstrate SEFI on spatially resolved gene expression profiles of the developing retina, acquired using multiplexed single molecule Fluorescence In Situ Hybridization (smFISH). SEFI is publicly available at https://github.com/eduardchelebian/sefi.

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)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [52.106879463828044]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention. Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification [0.19791587637442667]
  This study introduces a novel single-stage approach for generating image-label pairs to augment histology datasets.<n>Unlike state-of-the-art methods that utilize diffusion models with separate components for label and image generation, our approach employs a latent diffusion model.<n>This model enables tailored data generation by conditioning on user-defined parameters such as cell types, quantities, and tissue types.
  arXiv  Detail & Related papers  (2025-02-12T19:51:41Z)
• Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images [1.3124513975412255]
  spatial transcriptomics (ST) enables transcriptome-wide gene expression profiling while preserving spatial context. Current spatial clustering methods fail to fully integrate high-resolution histology image features with gene expression data. We propose a novel contrastive learning-based deep learning approach that integrates gene expression data with histology image features.
  arXiv  Detail & Related papers  (2024-10-31T00:32:24Z)
• MorphoSeg: An Uncertainty-Aware Deep Learning Method for Biomedical Segmentation of Complex Cellular Morphologies [5.50767638479269]
  Deep learning has revolutionized medical and biological imaging, particularly in segmentation tasks. segmenting biological cells remains challenging due to the high variability and complexity of cell shapes. We introduce a novel benchmark dataset of Ntera-2 cells, a pluripotent carcinoma cell line, exhibiting diverse morphologies across multiple stages of differentiation. We propose an uncertainty-aware deep learning framework for complex cellular morphology segmentation (MorphoSeg) by incorporating sampling of virtual outliers from low-likelihood regions during training.
  arXiv  Detail & Related papers  (2024-09-25T17:25:06Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy [0.20999222360659603]
  We developed a computational framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells.
  arXiv  Detail & Related papers  (2023-09-02T11:20:10Z)
• Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures [19.24727395217543]
  Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. We characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell.
  arXiv  Detail & Related papers  (2023-08-20T05:26:25Z)
• Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation [48.267001230607306]
  We propose a pretraining framework that enhances multiscale consistency in EM volumes. Our approach leverages a Siamese network architecture, integrating strong and weak data augmentations. It effectively captures voxel and feature consistency, showing promise for learning transferable representations for EM analysis.
  arXiv  Detail & Related papers  (2023-08-19T05:49:13Z)
• 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.