Automatic detection and counting of retina cell nuclei using deep learning

2020-02-10 · S. M. Hadi Hosseini, Hao Chen, Monica M. Jablonski

The ability to automatically detect, classify, calculate the size, number, and grade of retinal cells and other biological objects is critically important in eye disease like age-related macular degeneration (AMD). In this paper, we developed an automated tool based on deep learning technique and Mask R-CNN model to analyze large datasets of transmission electron microscopy (TEM) images and quantify retinal cells with high speed and precision. We considered three categories for outer nuclear layer (ONL) cells: live, intermediate, and pyknotic. We trained the model using a dataset of 24 samples. We then optimized the hyper-parameters using another set of 6 samples. The results of this research, after applying to the test datasets, demonstrated that our method is highly accurate for automatically detecting, categorizing, and counting cell nuclei in the ONL of the retina. Performance of our model was tested using general metrics: general mean average precision (mAP) for detection; and precision, recall, F1-score, and accuracy for categorizing and counting.

📄 PDF Abstract BibTeX arXiv:2002.03563

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Test 설명 없음

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

RPN A Region Proposal Network, or RPN, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

RoIAlign Region of Interest Align, or RoIAlign, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh…

Mask R-CNN Mask R-CNN extends Faster R-CNN to solve instance segmentation tasks. It achieves this by adding a branch for predicting an…

CoNIC: Colon Nuclei Identification and Counting Challenge 2022

2021-11-29 · Simon Graham, Mostafa Jahanifar, Quoc Dang Vu, Giorgos Hadjigeorghiou 외

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…

Explainable Models Nuclear Segmentation

3D Segmentation of Neuronal Nuclei and Cell-Type Identification using Multi-channel Information

2024-10-04 · Antonio LaTorre, Lidia Alonso-Nanclares, José María Peña, Javier De Felipe

Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of n…

3D Reconstruction Segmentation

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

2021-06-25 · Yibao Sun, Xingru Huang, Huiyu Zhou, Qianni Zhang

The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challeng…

Cell Detection Dense Object Detection object-detection Object Detection+1

Nuclei Detection Using Mixture Density Networks

2018-08-22 · Navid Alemi Koohababni, Mostafa Jahanifar, Ali Gooya, Nasir Rajpoot

Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc. This is a challenging task due to the c…

Cell Segmentation Image-Variation

Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting

2022-03-01 · Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu 외

Classification Explainable Models Nuclear Segmentation Segmentation