Esophagus segmentation from planning CT images using an atlas-based deep learning approach

Diniz, João Otávio Bandeira; Ferreira, Jonnison Lima; Diniz, Pedro Henrique Bandeira; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de

doi:10.1016/j.cmpb.2020.105685

Cited by 30 publications

(18 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Examples include convolutional layers, pooling layers, activation layers, and dropout layers. CNNs are being increasingly used in several problems involving medical imaging [10,14,15,17,18,55,56].…”

Section: Training Lung Segmentation Modelmentioning

confidence: 99%

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

Diniz

Quintanilha

Neto

et al. 2021

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT). CT images provide valuable information on how this new disease affects the lungs of patients. However, the analysis of these images is not trivial, especially when researchers are searching for quick solutions. Detecting and evaluating this disease can be tiring, time-consuming, and susceptible to errors. Thus, in this study, we aim to automatically segment infections caused by COVID19 and provide quantitative measures of these infections to specialists, thus serving as a support tool. We use a database of real clinical cases from Pedro Ernesto University Hospital of the State of Rio de Janeiro, Brazil. The method involves five steps: lung segmentation, segmentation and extraction of pulmonary vessels, infection segmentation, infection classification, and infection quantification. For the lung segmentation and infection segmentation tasks, we propose modifications to the traditional U-Net, including batch normalization, leaky ReLU, dropout, and residual block techniques, and name it as Residual U-Net. The proposed method yields an average Dice value of 77.1% and an average specificity of 99.76%. For quantification of infectious findings, the proposed method achieves results like that of specialists, and no measure presented a value of ρ < 0.05 in the paired t-test. The results demonstrate the potential of the proposed method as a tool to help medical professionals combat COVID-19. fight the COVID-19.

show abstract

Section: Training Lung Segmentation Modelmentioning

confidence: 99%

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

Diniz

Quintanilha

Neto

et al. 2021

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several studies focus on solving the problem of esophagus segmentation [ 4 , 5 , 6 , 7 , 8 ]. A model FCN [ 4 ] is used for segmentation of esophagus.…”

Section: Related Workmentioning

confidence: 99%

“…This method integrated a Channel Attention Module (CAM) and Cross-level Feature Fusion Module (CFFM) into a deep learning model to strengthen the generalization ability of the network by employing high-level features to low-level features. An atlas-based deep learning approach [ 7 ] is used to segment the esophagus. This method includes five main steps proposed for esophagus segmentation for better planning of radiotherapy in CT.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

Tran

Kim

Yang

et al. 2021

Sensors

View full text Add to dashboard Cite

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.

show abstract

“…Thus, many computational methods have been proposed to assist specialists. Computer-aided detection and diagnostics (CAD and CADx, respectively) play important roles in this task ( Diniz et al, 2018 , Diniz et al, 2018 , Diniz et al, 2019 , Souza et al, 2019 , Carvalho et al, 2020 , Cruz et al, 2020 , Diniz et al, 2020 ). Thus, the literature contains several CXR-based methods suitable for identifying patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost

Júnior

Cruz

Diniz

et al. 2021

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

show abstract

Esophagus segmentation from planning CT images using an atlas-based deep learning approach

Cited by 30 publications

References 32 publications

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost

Contact Info

Product

Resources

About