Cell nuclei segmentation in cytological images using convolutional neural network and seeded watershed algorithm
Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei
segmentation presents many challenges because they usually create complex clusters in …
segmentation presents many challenges because they usually create complex clusters in …
[PDF][PDF] Breast cancer nuclei segmentation and classification based on a deep learning approach
M Kowal, M Skobel, A Gramacki… - International Journal of …, 2021 - intapi.sciendo.com
One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy
without aspiration. Cell nuclei are the most important elements of cancer diagnostics based …
without aspiration. Cell nuclei are the most important elements of cancer diagnostics based …
An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images
Background Since nuclei segmentation in histopathology images can provide key
information for identifying the presence or stage of a disease, the images need to be …
information for identifying the presence or stage of a disease, the images need to be …
Segmentation of heavily clustered nuclei from histopathological images
Automated cell nucleus segmentation is the key to gain further insight into cell features and
functionality which support computer-aided pathology in early diagnosis of diseases such as …
functionality which support computer-aided pathology in early diagnosis of diseases such as …
MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information
Accurate segmentation of nuclei in digital pathology images can assist doctors in diagnosing
diseases and evaluating subsequent treatments. Manual segmentation of nuclei from …
diseases and evaluating subsequent treatments. Manual segmentation of nuclei from …
Cell nuclei detection and segmentation for computational pathology using deep learning
K Chen, N Zhang, L Powers… - 2019 Spring Simulation …, 2019 - ieeexplore.ieee.org
This work presents a deep learning model and image processing based processing flow to
detect and segment nuclei from microscopy images. This work aims at isolating each nuclei …
detect and segment nuclei from microscopy images. This work aims at isolating each nuclei …
Cell nucleus segmentation in color histopathological imagery using convolutional networks
B Pang, Y Zhang, Q Chen, Z Gao… - 2010 Chinese …, 2010 - ieeexplore.ieee.org
Recent studies have shown that convolutional networks can achieve a great deal of success
in high-level vision problems such as objection recognition. In this paper, convolutional …
in high-level vision problems such as objection recognition. In this paper, convolutional …
[HTML][HTML] Detecting and segmenting cell nuclei in two-dimensional microscopy images
Introduction: Cell nuclei are important indicators of cellular processes and diseases.
Segmentation is an essential stage in systems for quantitative analysis of nuclei extracted …
Segmentation is an essential stage in systems for quantitative analysis of nuclei extracted …
Segmentation of nuclei in histopathology images using fully convolutional deep neural architecture
VA Natarajan, MS Kumar, R Patan… - … on computing and …, 2020 - ieeexplore.ieee.org
Nuclei segmentation is an initial step in the automated analysis of digitized microscopic
images. This paper focuses on utilizing the LinkNET-34 architecture for semantic …
images. This paper focuses on utilizing the LinkNET-34 architecture for semantic …
Nuclei segmentation using attention aware and adversarial networks
E Goceri - Neurocomputing, 2024 - Elsevier
Accurate segmentation of nuclei plays a critical role in pathology since assessments and
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …