Grey Level Texture Features for Segmentation of Chromogenic Dye RNAscope From Breast Cancer Tissue

• URL: http://arxiv.org/abs/2401.15886v2
• Date: Tue, 12 Mar 2024 04:15:35 GMT
• Title: Grey Level Texture Features for Segmentation of Chromogenic Dye RNAscope From Breast Cancer Tissue
• Abstract summary: This paper investigates the usefulness of grey level texture features for automatically segmenting and classifying the positions of RNAscope transcripts. A small set of grey level features, including Grey Level Dependence Matrix and Neighbouring Grey Tone Difference Matrix features, were well suited for the task. The method performed similarly to expert annotators at identifying the positions of RNAscope transcripts, with an F1-score of 0.571 compared to the expert inter-rater F1-score of 0.596.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chromogenic RNAscope dye and haematoxylin staining of cancer tissue facilitates diagnosis of the cancer type and subsequent treatment, and fits well into existing pathology workflows. However, manual quantification of the RNAscope transcripts (dots), which signify gene expression, is prohibitively time consuming. In addition, there is a lack of verified supporting methods for quantification and analysis. This paper investigates the usefulness of grey level texture features for automatically segmenting and classifying the positions of RNAscope transcripts from breast cancer tissue. Feature analysis showed that a small set of grey level features, including Grey Level Dependence Matrix and Neighbouring Grey Tone Difference Matrix features, were well suited for the task. The automated method performed similarly to expert annotators at identifying the positions of RNAscope transcripts, with an F1-score of 0.571 compared to the expert inter-rater F1-score of 0.596. These results demonstrate the potential of grey level texture features for automated quantification of RNAscope in the pathology workflow.

Related papers

• Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification [1.8607003949195091]
  We show that color features alone can achieve strong performance in binary diagnostic tasks.<n>This performance is likely attributable to global chromatic shifts associated with malignancy.<n>This study suggests that simple, computationally efficient color features can serve as an effective pre-screening tool.
  arXiv  Detail & Related papers  (2026-05-18T15:10:04Z)
• Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework [6.0791593833288085]
  A deep ultraviolet fluorescence scanning microscope (DUV-FSM) enables rapid acquisition of whole surface images (WSIs) for excised tissue.<n>This study introduces a DUV WSI classification framework using a patch-level vision transformer (ViT) model, capturing local and global features.<n>A comprehensive 5-fold cross-validation demonstrates the proposed approach significantly outperforms conventional deep learning methods, achieving a classification accuracy of 98.33%.
  arXiv  Detail & Related papers  (2025-05-12T15:22:54Z)
• GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology [14.812589661794592]
  GRAPHITE is a post-hoc explainable framework designed for breast cancer tissue microarray (TMA) analysis. We trained the model on 140 tumour TMA cores and four benign whole slide images from which 140 benign samples were created, and tested it on 53 pathologist-annotated TMA samples. It achieved a mean average precision (mAP) of 0.56, an area under the receiver operating characteristic curve (AUROC) of 0.94, and a threshold robustness (ThR) of 0.70.
  arXiv  Detail & Related papers  (2025-01-08T00:54:43Z)
• Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development [59.74920439478643]
  In this paper, we collect and annotated the first benchmark dataset that covers diverse ERUS scenarios. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. We introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR)
  arXiv  Detail & Related papers  (2024-08-19T15:04:42Z)
• Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks [6.869831177092736]
  We analyzed scRNA-seq data from 181 tumor biopsies across 13 cancer types. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was performed to identify relevant gene sets. Oncogenes from OncoKB evaluated with deep learning models, including multilayer perceptrons (MLPs) and graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2024-08-13T23:24:36Z)
• A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method [0.0]
  Breast cancer classification stands as a pivotal pillar in ensuring timely diagnosis and effective treatment. This study harnesses the synergistic capabilities of colour space ensembling and quantum-classical stacking to elevate the precision of breast cancer classification.
  arXiv  Detail & Related papers  (2024-04-03T04:26:50Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer [7.607669435880715]
  Graph based features are applied to gastric cancer histopathology microscopic image analysis. It is found that using U-Net to segment tissue areas, then extracting graph based features, and finally using RBF SVM classifier gives the optimal results with 94.29%.
  arXiv  Detail & Related papers  (2022-05-17T16:16:29Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications [3.4276717980242553]
  It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold. It is difficult using a single traditional image processing algorithm to extract continuous and accurate contour of a porous bioelastomer. This paper proposes a joint algorithm for the bioelastomer's contour detection and a texture feature extraction method for monitoring the degradation behavior of the bioelastomer.
  arXiv  Detail & Related papers  (2022-01-28T08:42:58Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Topological Data Analysis of copy number alterations in cancer [70.85487611525896]
  We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
  arXiv  Detail & Related papers  (2020-11-22T17:31:23Z)
• Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology [0.0]
  We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. With achieving state-of-the-art test accuracy of 96.2% we also embark on using deployment friendly architecture called SqueezeNet for memory-limited hardware.
  arXiv  Detail & Related papers  (2020-04-03T15:05:36Z)

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.