Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images

By Yousef Al-Kofahi, Wiem Lassoued, William Lee, B. Roysam. IEEE Transactions on Biomedical Engineering, 2010

716 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/63a373063d51489b31e07ee639ab74b6cf586240

How researchers see this paper

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

In Artificial Intelligence

1. Lin F. Yang
2. Fuyong Xing
3. Anant Madabhushi
4. Nasir M. Rajpoot
5. Peter Naylor
6. Thomas Walter
7. Fabien Reyal
8. Marick Lae

In Biophysics

1. Fuyong Xing
2. Lin F. Yang
3. Peter Naylor
4. Thomas Walter
5. Fabien Reyal
6. Marick Lae
7. Maciej A. Mazurowski
8. Mateusz Buda

In Computer Vision and Pattern Recognition

1. Lin F. Yang
2. Fuyong Xing
3. Anant Madabhushi
4. Nasir M. Rajpoot
5. Peter Naylor
6. Thomas Walter
7. Fabien Reyal
8. Marick Lae

Citations in the corpus

• AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, neutral): “Bamford and Lovell, for example, used active contours to segment nuclei in Pap stained cell images [163] [167].”
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[68] have presented a two-stage graph cutbased method to segment nuclei in histopathology images.”
• Breast Cancer Histopathology Image Analysis: A Review (2014, neutral): “, fluorescence microscopy) can also be applied to this problem [69]–[77].”
• A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images (2018, methodology): “Furthermore, a few filtering-based methods have been proposed that utilize the features of the nuclei, such as Laplacian-of-Gaussian (LoG) filters [1] and fast radial symmetry transformation [32].”
• Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images (2016, methodology): “Al Kofahi et al. [14] proposed a graph cut-based method that is initialized using response of the image to Laplacian of Gaussian (LoG) filter.”
• CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “MEDICAL image segmentation is often an important step in medical image analysis, such as optic disc segmentation [1]–[3] and blood vessel detection [4]–[8] in retinal images, cell segmentation [9]–[11] in electron microscopic (EM) recordings, lung segmentation [12]–[16] and brain segmentation [17]–[”
• 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).”
• 3D Cell Nuclear Morphology: Microscopy Imaging Dataset And Voxel-Based Morphometry Classification Results (2017, methodology): “After testing a number of implementations of 3D thresholding-based and watershed-like methods in commonly used bioimage analysis packages, we perform the automatic 3D segmentation of nuclei using Nuclear Segmentation algorithm from the Farsight toolkit [1].”
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, background): “In radiology, detection is often an important step in the diagnostic process which identifies an abnormality (such as a mass or a nodule), an organ, an anatomical structure, or a region of interest for further classification or segmentation [84, 85, 86].”
• Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, methodology): “[58] proposed a distance-constrained multiscale LoG filtering method to identify the center of nuclei by exploiting shape and size cues available in the Euclidean distance map of the binarized image.”
• Cell Segmentation: 50 Years Down the Road [Life Sciences] (2012, methodology): “The method used here involves graph-cuts-based binarization, Laplacian-of-Gaussian-based cell detection (see red dots), and marker-based clustering (images from [14] and used with permission).”
• Digital imaging in pathology: whole-slide imaging and beyond. (2013, background): “Roysam and colleagues (79) have developed an open source–based cytometric analysis system (Farsight; see http://farsight-toolkit.”