scholarly journals A Conditional Random Field Weakly Supervised Segmentation Approach for Segmenting keratocytes Cells in Corneal Optical Coherence Tomography Images

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Ameneh Boroomand ◽  
Alexander Wong ◽  
Kostadinka Bizheva
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Automatic Segmentation ◽  
Cell Segmentation ◽  
Human Cornea ◽  
Optical Coherence ◽  
Sparse Model ◽  
Supervised Segmentation ◽  
Segmentation Approach ◽  
Weakly Supervised

<p>Keratocytes are vital for maintaining the overall health of human<br />cornea as they preserve the corneal transparency and help in healing<br />corneal injuries. Manual segmentation of keratocytes is challenging,<br />time consuming and also needs an expert. Here, we propose<br />a novel semi-automatic segmentation framework, called Conditional<br />Random FieldWeakly Supervised Segmentation (CRF-WSS)<br />to perform the keratocytes cell segmentation. The proposed framework<br />exploits the concept of dictionary learning in a sparse model<br />along with the Conditional Random Field (CRF) modeling to segment<br />keratocytes cells in Ultra High Resolution Optical Coherence<br />Tomography (UHR-OCT) images of human cornea. The results<br />show higher accuracy for the proposed CRF-WSS framework compare<br />to the other tested Supervised Segmentation (SS) andWeakly<br />Supervised Segmentation (WSS) methods.</p>

scholarly journals Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images

2013 ◽  
Vol 4 (10) ◽  
pp. 2032 ◽  
Author(s):  
Ameneh Boroomand ◽  
Alexander Wong ◽  
Edward Li ◽  
Daniel S. Cho ◽  
Betty Ni ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Random Field ◽  
Conditional Random Field ◽  
Optical Coherence ◽  
Field Approach

scholarly journals Automatic Segmentation of Cervical Nuclei Based on Deep Learning and a Conditional Random Field

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 53709-53721 ◽  
Author(s):  
Yiming Liu ◽  
Pengcheng Zhang ◽  
Qingche Song ◽  
Andi Li ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Field ◽  
Conditional Random Field ◽  
Automatic Segmentation

scholarly journals Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach

2019 ◽  
Author(s):  
Bin Wang ◽  
Guojun Qi ◽  
Sheng Tang ◽  
Tianzhu Zhang ◽  
Yunchao Wei ◽  
...  
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Semantic Segmentation ◽  
Semantic Feature ◽  
Feature Maps ◽  
Feature Enhancement ◽  
Pascal Voc ◽  
Segmentation Approach ◽  
Fully Connected

Semantic segmentation suffers from the fact that densely annotated masks are expensive to obtain. To tackle this problem, we aim at learning to segment by only leveraging scribbles that are much easier to collect for supervision. To fully explore the limited pixel-level annotations from scribbles, we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic components, i.e., prediction refinement and boundary regression. Specifically, the prediction refinement progressively makes a better segmentation by adopting an iterative upsampling and a semantic feature  enhancement strategy. In the boundary regression, we employ class-agnostic edge maps for supervision to effectively guide the segmentation network in localizing the boundaries between different semantic regions, leading to producing finer-grained representation of feature maps for semantic segmentation. The experiment results on the PASCAL VOC 2012 demonstrate the proposed BPG achieves mIoU of 73.2% without fully connected Conditional Random Field (CRF) and 76.0% with CRF, setting up the new state-of-the-art in literature.

scholarly journals Improving the automatic segmentation of subtitles through conditional random field

2017 ◽  
Vol 88 ◽  
pp. 83-95 ◽  
Author(s):  
Aitor Álvarez ◽  
Carlos-D. Martínez-Hinarejos ◽  
Haritz Arzelus ◽  
Marina Balenciaga ◽  
Arantza del Pozo
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Automatic Segmentation

Automatic Segmentation of Cell Images by Improved Graph Cut-Based Approach

2016 ◽  
Vol 29 ◽  
pp. 74-80 ◽  
Author(s):  
Kazeem Oyeyemi Oyebode ◽  
Jules Raymond Tapamo
Keyword(s):  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Graph Cut ◽  
Cell Segmentation ◽  
Otsu Thresholding ◽  
Segmentation Approach ◽  
Fully Automatic ◽  
Public Datasets ◽  
Graph Cut Segmentation ◽  
Selection Of

Cell segmentation provides an opportunity to reveal object of interest from the background of an image. In the traditional graph cut segmentation approach, the user initiates the segmentation process by selecting pixels for foreground and background. However, one of the problems of traditional graph cut is that it is time consuming, especially on a large dataset. Thus, we propose a fully automatic technique for cell segmentation on graph cut to automate the selection of sample foreground and background pixels. In order to achieve this, a combination of two methods namely Otsu thresholding and kmeans clustering algorithm is explored. The Otsu thresholding and the k-means provides an initial cell segmentation, creating a platform to automatically select sample foreground and background pixels initiating the graph cut segmentation. Experimental results on two public datasets suggest promising results.

scholarly journals Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Average Error ◽  
Good Prediction ◽  
Optical Coherence ◽  
Learning Method ◽  
Subfoveal Choroidal Thickness ◽  
Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

Sign in / Sign up

Export Citation Format

Share Document