Automatic Segmentation and Measurement of Pleural Effusions on CT

Yao, Jianhua; Bliton, John; Summers, Ronald M.

doi:10.1109/tbme.2013.2243446

Cited by 19 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, computer vision methods have been used for automated pleural effusion segmentation on limited CT sample sizes. 9,10 The proposed segmentation algorithm provides robust volumetric results in a large and heterogeneous clinical sample and therefore might have implications for clinical use and offers the potential for prognostication. 19 The segmentation algorithm was validated on the PleThora dataset, 24 consisting of tumor-associated effusions, and provided a good volumetry with an ICC of 0.97.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with radiography, CT provides accurate pleural effusion quantification; nevertheless, in radiology reports, effusions are commonly described only qualitatively because manual delineation is time-consuming. Automated quantification methods based on traditional image processing or atlas segmentation have resulted in moderate performance, have not included effusion-free control cohorts, or had limited sample sizes 9,10 …”

mentioning

confidence: 99%

“…Automated quantification methods based on traditional image processing or atlas segmentation have resulted in moderate performance, have not included effusion-free control cohorts, or had limited sample sizes. 9,10 Computed tomography is especially relevant for a detailed assessment of effusion subtypes (ie, hemothorax, empyema, malignant effusion, and pneumothorax) and for detection of the causative diagnosis. 11 Additional pleural complexity features, such as hyperdense fluid, pleural thickening, gas, and loculation, are used to differentiate between serous and these more complex effusion subtypes [12][13][14] (from now on referred to as simple and complex effusions, respectively).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Automated Detection, Segmentation, and Classification of Pleural Effusion From Computed Tomography Scans Using Machine Learning

et al. 2022

View full text Add to dashboard Cite

ObjectiveThis study trained and evaluated algorithms to detect, segment, and classify simple and complex pleural effusions on computed tomography (CT) scans.Materials and MethodsFor detection and segmentation, we randomly selected 160 chest CT scans out of all consecutive patients (January 2016–January 2021, n = 2659) with reported pleural effusion. Effusions were manually segmented and a negative cohort of chest CTs from 160 patients without effusions was added. A deep convolutional neural network (nnU-Net) was trained and cross-validated (n = 224; 70%) for segmentation and tested on a separate subset (n = 96; 30%) with the same distribution of reported pleural complexity features as in the training cohort (eg, hyperdense fluid, gas, pleural thickening and loculation). On a separate consecutive cohort with a high prevalence of pleural complexity features (n = 335), a random forest model was implemented for classification of segmented effusions with Hounsfield unit thresholds, density distribution, and radiomics-based features as input. As performance measures, sensitivity, specificity, and area under the curves (AUCs) for detection/classifier evaluation (per-case level) and Dice coefficient and volume analysis for the segmentation task were used.ResultsSensitivity and specificity for detection of effusion were excellent at 0.99 and 0.98, respectively (n = 96; AUC, 0.996, test data). Segmentation was robust (median Dice, 0.89; median absolute volume difference, 13 mL), irrespective of size, complexity, or contrast phase. The sensitivity, specificity, and AUC for classification in simple versus complex effusions were 0.67, 0.75, and 0.77, respectively.ConclusionUsing a dataset with different degrees of complexity, a robust model was developed for the detection, segmentation, and classification of effusion subtypes. The algorithms are openly available at https://github.com/usb-radiology/pleuraleffusion.git.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Automated Detection, Segmentation, and Classification of Pleural Effusion From Computed Tomography Scans Using Machine Learning

et al. 2022

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, this is the first study conducted on extracting lung parenchyma from CT images using a fully machine learning-based framework, rather than the whole lung or various lung pathologies. This idea originated from one previously ignored fact that lung parenchyma is quite different from lung pathologies [11, 12, 41]. The lung parenchyma owns commonalities across subjects, diseases and CT scanners although lung pathologies exist under various appearances.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset

Yue

et al. 2019

BioMed Eng OnLine

View full text Add to dashboard Cite

BackgroundLung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning.MethodsWe proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods.ResultsA total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96.ConclusionsThe results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.

show abstract

“…Adaptive thresholding of the Otsu is used along with morphological opening as well as closing to lung parenchyma segment suggested by [23]. Morphological filtering with fixed size structuring element, however, will cause major edge distortions because of lung boundary smoothing, which decreases the accuracy of segmentation.…”

Section: Fig 5 Limitations Of Segmentation Strategies Of Gray Level Thresholdingmentioning

confidence: 99%

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Bhandary¹,

G²,

Basthikodi³

et al. 2021

IJETT

View full text Add to dashboard Cite

Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we proposed a novel methodology for lung Segmentation on the basis of Fuzzy C-Means Clustering, Adaptive Thresholding, and Segmentation of Active Contour Model. The experimental results are analysed and presented.

show abstract

Automatic Segmentation and Measurement of Pleural Effusions on CT

Cited by 19 publications

References 22 publications

Automated Detection, Segmentation, and Classification of Pleural Effusion From Computed Tomography Scans Using Machine Learning

Automated Detection, Segmentation, and Classification of Pleural Effusion From Computed Tomography Scans Using Machine Learning

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Contact Info

Product

Resources

About