scholarly journals Impact of Early Intravenous Haemostatic Drugs on Brain Haemorrhage Patients and Their Image Segmentation Based on RGB-D Images

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Zhenzhen Wang ◽  
Yating Mou ◽  
Hao Li ◽  
Rui Yang ◽  
Yanxun Jia
Keyword(s):  
Stereo Matching ◽  
Factor Viia ◽  
Coagulation Factor ◽  
Cerebral Haemorrhage ◽  
Semantic Segmentation ◽  
Percentage Error ◽  
Manual Segmentation ◽  
Segmentation Accuracy ◽  
Random Number Table ◽  
Validation Set

Cerebral haemorrhage is a serious subtype of stroke, with most patients experiencing short-term haematoma enlargement leading to worsening neurological symptoms and death. The main hemostatic agents currently used for cerebral haemorrhage are antifibrinolytics and recombinant coagulation factor VIIa. However, there is no clinical evidence that patients with cerebral haemorrhage can benefit from hemostatic treatment. We provide an overview of the mechanisms of haematoma expansion in cerebral haemorrhage and the progress of research on commonly used hemostatic drugs. To improve the semantic segmentation accuracy of cerebral haemorrhage, a segmentation method based on RGB-D images is proposed. Firstly, the parallax map was obtained based on a semiglobal stereo matching algorithm and fused with RGB images to form a four-channel RGB-D image to build a sample library. Secondly, the networks were trained with 2 different learning rate adjustment strategies for 2 different structures of convolutional neural networks. Finally, the trained networks were tested and compared for analysis. The 146 head CT images from the Chinese intracranial haemorrhage image database were divided into a training set and a test set using the random number table method. The validation set was divided into four methods: manual segmentation, algorithmic segmentation, the exact Tada formula, and the traditional Tada formula to measure the haematoma volume. The manual segmentation was used as the “gold standard,” and the other three algorithms were tested for consistency. The results showed that the algorithmic segmentation had the lowest percentage error of 15.54 (8.41, 23.18) % compared to the Tada formula method.

scholarly journals Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images

2020 ◽  
Author(s):  
Ali Hatamizadeh ◽  
Demetri Terzopoulos ◽  
Andriy Myronenko
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Practitioner ◽  
Semantic Segmentation ◽  
Manual Segmentation ◽  
Shape Information ◽  
Texture Information ◽  
Segmentation Accuracy

AbstractTextures and edges contribute different information to image recognition. Edges and boundaries encode shape information, while textures manifest the appearance of regions. Despite the success of Convolutional Neural Networks (CNNs) in computer vision and medical image analysis applications, predominantly only texture abstractions are learned, which often leads to imprecise boundary delineations. In medical imaging, expert manual segmentation often relies on organ boundaries; for example, to manually segment a liver, a medical practitioner usually identifies edges first and subsequently fills in the segmentation mask. Motivated by these observations, we propose a plug-and-play module, dubbed Edge-Gated CNNs (EG-CNNs), that can be used with existing encoder-decoder architectures to process both edge and texture information. The EG-CNN learns to emphasize the edges in the encoder, to predict crisp boundaries by an auxiliary edge supervision, and to fuse its output with the original CNN output. We evaluate the effectiveness of the EG-CNN with various mainstream CNNs on two publicly available datasets, BraTS 19 and KiTS 19 for brain tumor and kidney semantic segmentation. We demonstrate how the addition of EG-CNN consistently improves segmentation accuracy and generalization performance.

scholarly journals A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms

2021 ◽  
Vol 32 (3) ◽  
Author(s):  
Dimitrios Bellos ◽  
Mark Basham ◽  
Tony Pridmore ◽  
Andrew P. French
Keyword(s):  
Time Series ◽  
Knowledge Transfer ◽  
Semantic Segmentation ◽  
Real World Data ◽  
Transfer Scheme ◽  
X Ray ◽  
Segmentation Accuracy ◽  
X Ray Computed ◽  
Temporal Events ◽  
Time Critical

AbstractOver recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke

2021 ◽  
Vol 2099 (1) ◽  
pp. 012021
Author(s):  
A V Dobshik ◽  
A A Tulupov ◽  
V B Berikov
Keyword(s):  
Computed Tomography ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Training Data ◽  
Neural Network Architecture ◽  
Brain Images ◽  
Tomographic Images ◽  
Computed Tomography Images ◽  
Segmentation Accuracy ◽  
Weakly Supervised

Abstract This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke in the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architecture with several modifications. Experiments on real computed tomography scans show that the proposed methods increase the segmentation accuracy.

A Novel Exosite on Coagulation Factor VIIa and Its Molecular Interactions with a New Class of Peptide Inhibitors‡

Biochemistry ◽  
2001 ◽  
Vol 40 (32) ◽  
pp. 9522-9531 ◽  
Author(s):  
Martin Roberge ◽  
Lydia Santell ◽  
Mark S. Dennis ◽  
Charles Eigenbrot ◽  
Mary A. Dwyer ◽  
...  
Keyword(s):  
Molecular Interactions ◽  
Factor Viia ◽  
Coagulation Factor ◽  
Peptide Inhibitors ◽  
New Class
Sign in / Sign up

Export Citation Format

Share Document