Pathological lung segmentation in chest CT images based on improved random walker

et al. 2021

Preprint

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Background: Phase-Contrast Angiography (PCA) is an acceptable magnetic resonance imaging method for cerebrovascular diseases diagnosis. However, it is an important and great challenge to accurately extract cerebrovascular structures from PCA images because of the complex vascular structures and large amount of noise. To accomplish this task, this work proposes a cerebrovascular segmentation algorithm based on Local Binary Fitting (LBF) and Hidden Markov Model (HMM), which can accurately extraction features from PCA data. Results: Dice Similarity Coefficient (DSC), False Positive Score (FPN), and False Negative Score (FTN) are defined as metrics to assess this algorithm. Results show this method obtain higher accuracy (74.58%, 4.93%, 24.48%) than compared methods. Conclusion: Based on quantitative results, it appears that the proposed method has a higher accuracy rate compared to other methods. Due to no human correction and has no training process, it performs well on small datasets. Thus, this algorithm can accord with clinical requirements.

Section: Resultsmentioning

confidence: 99%

Cerebrovascular Segmentation Based on Hidden Markov Model in Phase-Contrast Angiography Images

et al. 2021

Preprint

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“…Sometimes, infection is located at lungs borders adhere to tissues, resulting in blurring and rupture of the lung verge. Therefore, Random Walker method [25] is adopted to complete pathological lung extraction. Firstly, structured data are established and defined as V = (P, E) based on lung CT images.…”

Section: Lung Mask Preparationmentioning

confidence: 99%

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images

et al. 2021

Diagnostics

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(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94–00.02%, 60.42–11.25%, 70.79–09.35% and 63.15–08.35%) and public dataset (99.73–00.12%, 77.02–06.06%, 41.23–08.61% and 52.50–08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.

“…Because of the pandemic and the shortage of medical resources, the efficiency and accuracy of disease diagnosis present serious challenges [11][12][13]. By CT images segmentation, intuitive three-dimensional structures and accurate digital models can be obtained [14]. It can quickly locate suspicious COVID-19 lesions and calculate quantitative data, such as volume, shape, and density of the lesions.…”

Section: Introductionmentioning

confidence: 99%

An Effective Deep Neural Network for Lung Lesions Segmentation From COVID-19 CT Images

Zha

et al. 2021

IEEE Trans. Ind. Inf.

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Automatic segmentation of lung lesions from COVID-19 Computed Tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this work provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: Firstly, the proposed Region of Interest (ROI) extraction implements patch mechanism strategy to satisfy the applicability of 3D network and remove irrelevant background. Secondly, 3D network is established to extract spatial features, where 3D Attention model promotes network to enhance target area. Then, to improve convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and Conditional Random Field (CRF) are applied to realize data resampling and binary segmentation. This method was assessed with some comparative Experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.