Segmentation of Clustered Nuclei With Shape Markers and Marking Function

By Jierong Cheng, Jagath Rajapakse. IEEE Transactions on Biomedical Engineering, 2009

328 citations recorded by the source corpus.

We present a method to separate clustered nuclei from fluorescence microscopy cellular images, using shape markers and marking function in a watershed-like algorithm. Shape markers are extracted using an adaptive H-minima transform. A marking function based on the outer distance transform is introduced to accurately separate clustered nuclei. With synthetic images, we quantitatively demonstrate the performance of our method and provide comparisons with existing approaches. On mouse neuronal and Drosophila cellular images, we achieved 6%-7% improvement of segmentation accuracies over earlier methods.

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

View Paper: https://www.semanticscholar.org/paper/de844fed270218dfebf43c4414c0072bb5d98243

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Peter Naylor
2. Thomas Walter
3. Fabien Reyal
4. Marick Lae
5. Lin F. Yang
6. Robert Haase
7. Uwe Schmidt
8. Yuanpu Xie

In Computer Vision and Pattern Recognition

1. Lin F. Yang
2. Peter Naylor
3. Thomas Walter
4. Fabien Reyal
5. Marick Lae
6. Fuyong Xing
7. Yuanpu Xie
8. Robert Haase

In Media Technology

1. Lin F. Yang
2. Uwe Schmidt
3. Yuanpu Xie
4. Fuyong Xing

Citations in the corpus

• An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, methodology): “In addition, Cheng and Rajapakes [6] have employed an adaptive H-minima transform [7] to extract nucleus markers for the subsequent watershed segmentation in Drosophila Kc167 cell images, Schmitt and Hasse [8] have detected the markers by localizing the gravitation centers of mass clumped cells with”
• Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map (2019, background): “has been addressed by many authors with a variety of traditional approaches, including mathematical morphology [13], [19]–[21], pixel classification [22], level sets [23] and graph-based segmentation methods [24] (see also [25], [26] for extensive reviews).”
• CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “For instance, the works of Cheng and Rajapakse [24], Veta et al. [25], and Ali and Madabhushi [26] rely on a predefined nuclei geometry and the watershed algorithm to separate clustered nuclei, while Wienert et al. [27] used morphological operations without watershed and Liao et al. [28] utilized…”
• Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy (2019, methodology): “These include classical thresholding approaches with pixel-grouping via connected component, morphological methods based on the watershed transform [4, 16, 7], and optimization via graphcuts [5].”
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[44] 53 mouse neuronal and 4 Drosophila Kc167 cell images HAT watershed CSR/USR/OSR: 97.”
• Comparison of parameter‐adapted segmentation methods for fluorescence micrographs (2011, methodology): “Cheng and Rajapakse (30) proposed a segmentation method for neuronal as well as Drosophila cell fluorescence micrographs with a level set function in which the image energy is defined using intensity variances inside and outside of the contour.”