Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

By S. Naik, Scott Doyle, S. Agner, Anant Madabhushi, M. Feldman, John Tomaszewski. IEEE International Symposium on Biomedical Imaging, 2008

364 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/856a3c76453a798556096cd23848402d1351c1f9

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Citations in the corpus

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017, background): “While some approaches have looked at the problem of classifying images as either containing cancer or not 25,26 , no approach that we are aware of has tackled the problem of automated delineation of invasive carcinoma on whole slide images.”
INet: Convolutional Networks for Biomedical Image Segmentation (2021, background): “Segmenting regions of interest enables doctors to analyze only the signiﬁcant parts of otherwise incomprehensible multimodal biomedical images [18].”
Histopathological Image Analysis: A Review (2009, background): “6 shows some of the textural image features for discriminating between benign breast epithelial tissue [77] (DCIS,”
Histology image analysis for carcinoma detection and grading (2012, methodology): “…study has been extensively employed for cancer detection and grading applications, including prostate [41,42,105,106,108], breast [6,43,109], cervix [56,57,79,122], and lung [75,78,87,131] cancer grading, neuroblastoma categorization [61,81,83,84], and follicular lymphoma grading…”
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles (2016, methodology): “…and Tomaszewski, 2008; Kong et al., 2007; Kwak et al., 2011; Land et al., 2008; Lessmann et al., 2007; Meng et al., 2010; Monaco et al., 2010; Naik et al., 2008, 2007; Ozolek et al., 2014; Petushi et al., 2006; Qureshi et al., 2008, 2007; Sboner et al., 2003; Schnorrenberg et al., 1997;…”
Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (2024, background): “Traditional techniques based on background subtraction and color threshold [27, 29] fail to generalize to complex scenarios such as overlap and occlusion in histopathology images.”
Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation (2021, methodology): “This motivates the development of automated computer-aided diagnosis systems, which enable 1 To appear in Computers in Biology and Medicine , 2021 physicians to analyze speciﬁc image regions, especially in multimodal medical images [6].”
Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach (2012, background): “In Equation (5)–(7) let C1 be the start point, C2 the end point and r the length of the potential cutting line C1C2.”
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation (2019, background): “This enables the doctors to analyze only the signiﬁcant parts of the otherwise incomprehensible multimodal medical images [5].”
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014, methodology): “Naik et al., 5 presented a methodology for automated detection and segmentation of glands and nuclei in BCa histopathology images.”