Histological Images Segmentation by Convolutional Neural Network with Morphological Post-filtration

2019 ◽  
pp. 295-319
Author(s):  
Vladimir Khryashchev ◽  
Anton Lebedev ◽  
Olga Stepanova ◽  
Anastasiya Srednyakova
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Histological Images

scholarly journals Automated Localization and Segmentation of Mononuclear Cell Aggregates in Kidney Histological Images Using Deep Learning

2019 ◽  
Author(s):  
Dmytro S. Lituiev ◽  
Sung Jik Cha ◽  
Aaron Chin ◽  
Benjamin S. Glicksberg ◽  
Andrew Bishara ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Immune Cell ◽  
Deep Convolutional Neural Network ◽  
Markers Of Inflammation ◽  
Automated Method ◽  
Allograft Kidney ◽  
Histological Images ◽  
Validation Set ◽  
Lymphoid Aggregates

AbstractAllograft rejection is a major concern in kidney transplantation. Inflammatory processes in patients with kidney allografts involve various patterns of immune cell recruitment and distributions. Lymphoid aggregates (LAs) are commonly observed in patients with kidney allografts and their presence and localization may correlate with severity of acute rejection. Alongside with other markers of inflammation, LAs assessment is currently performed by pathologists manually in a qualitative way, which is both time consuming and far from precise. Here we present the first automated method of identifying LAs and measuring their densities in whole slide images of transplant kidney biopsies. We trained a deep convolutional neural network based on U-Net on 44 core needle kidney biopsy slides, monitoring loss on a validation set (n=7 slides). The model was subsequently tested on a hold-out set (n=10 slides). We found that the coarse pattern of LAs localization agrees between the annotations and predictions, which is reflected by high correlation between the annotated and predicted fraction of LAs area per slide (Pearson R of 0.9756). Furthermore, the network achieves an auROC of 97.78 ± 0.93% and an IoU score of 69.72 ± 6.24 % per LA-containing slide in the test set. Our study demonstrates that a deep convolutional neural network can accurately identify lymphoid aggregates in digitized histological slides of kidney. This study presents a first automatic DL-based approach for quantifying inflammation marks in allograft kidney, which can greatly improve precision and speed of assessment of allograft kidney biopsies when implemented as a part of computer-aided diagnosis system.

scholarly journals Segmentation of histological images and fibrosis identification with a convolutional neural network

2018 ◽  
Vol 98 ◽  
pp. 147-158 ◽  
Author(s):  
Xiaohang Fu ◽  
Tong Liu ◽  
Zhaohan Xiong ◽  
Bruce H. Smaill ◽  
Martin K. Stiles ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Histological Images

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

No-Reference Utility Estimation with a Convolutional Neural Network

2018 ◽  
Vol 2018 (9) ◽  
pp. 202-1-202-6 ◽  
Author(s):  
Edward T. Scott ◽  
Sheila S. Hemami
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Utility Estimation

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN)

2018 ◽  
Vol 2018 (10) ◽  
pp. 338-1-338-6
Author(s):  
Patrick Martell ◽  
Vijayan Asari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Defect Detection of Axle Box Cover Device Fixing Bolts in Metro Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Yao Yang ◽  
Yuanjiang Hu ◽  
Lingling Chen ◽  
Xiaoman Liu ◽  
Na Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection
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