Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach

By S. Wienert, Daniel Heim, K. Saeger, A. Stenzinger, M. Beil, P. Hufnagl, M. Dietel, Carsten Denkert, Frederick Klauschen. 2012

247 citations recorded by the source corpus.

Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approaches use a priori information on cell shape features to obtain the segmentation, which may introduce a bias favouring the detection of cell nuclei only with certain properties. In this study we present a novel contour-based “minimum-model” cell detection and segmentation approach that uses minimal a priori information and detects contours independent of their shape. This approach avoids a segmentation bias with respect to shape features and allows for an accurate segmentation (precision = 0.908; recall = 0.859; validation based on ∼8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology, Medical Imaging

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Citations in the corpus

Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net) (2018, methodology): “This approach uses minimal a priori information and detects contours independent of their shape [12].”
A Multi-Organ Nucleus Segmentation Challenge (2020, methodology): “[23] 600× 600 36 7,931 5 Individual nuclei boundary coordinates Naylor et al.”
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018, methodology): “Thus, we recommend the use of metrics that explicitly count correctly segmented objects as true positives and penalize any instance-level error, similar to practice in diagnostic applications (20,21).”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[182] 7931 cells of breast cancer, bone marrow, liver, kidney, and intestinal mucosa contour tracing, color deconvolution, thresholding precision/recall/conglomerate score: 90.”
Breast Cancer Histopathology Image Analysis: A Review (2014, unknown): “along points of high concavity [59], [60].”
Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images (2013, methodology): “In addition to the evaluation on our dataset, we evaluated the proposed method on a publicly available dataset used in a recently published paper on nuclei detection and segmentation [9].”
CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “The primary challenge is separating overlapping nuclei, and di ff erent techniques have been devised to do this [21, 22, 23, 24, 25, 26, 27, 28].”
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, methodology): “of schemes taking into account concavity point detection [28], [51], [57], [60], [61], [68], distance transform [11], [26], [54], marker-controlled watershed [9], [19], [24], [25], [59], adaptive”
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images (2018, methodology): “For instance, [14], [15] and [16] computed the concavity of nuclear clusters, while [17] used eclipse-ﬁtting to separate the clusters.”