High Throughput Lung and Lobar Segmentation by 2D and 3D CNN on Chest CT with Diffuse Lung Disease

Wang, Xiaoyong; Teng, Pang-yu; Lo, Pechin; Banola, Ashley; Kim, Grace H.; Abtin, Fereidoun; Goldin, Jonathan G.; Brown, Matthew S.

doi:10.1007/978-3-030-00946-5_21

Cited by 13 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the lung segmentation task, a model that automatically segments the lung lobes and lung airway trees can help radiologists improve efficiency and accuracy and optimize the diagnosis process in actual clinical applications. In previous studies, the use of DL algorithms to train the model is more common for the segmentation of pulmonary lobes, with an average accuracy of over 0.9 (38,(41)(42)(43)(44). In pulmonary airway tree segmentation, some traditional image processing algorithms are usually used, such as the region growing algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…In the previous studies of pulmonary lobe segmentation, there has been some difficulty in segmenting the right middle lobe, with a Dice coefficient ranging from 0.85 to 0.94 when employing a DL algorithm (38,(41)(42)(43)(44) and the traditional image processing approaches to gain a lower performance in the right middle lobe (45). Our current model has a high Dice coefficient in segmenting the right middle lobe, 0.964 in the test set.…”

mentioning

confidence: 92%

See 1 more Smart Citation

A deep learning-based post-processing method for automated pulmonary lobe and airway trees segmentation using chest CT images in PET/CT

Xing¹,

Zhang²,

Nie³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: The proposed algorithm could support accurate localization of lung disease. To develop and validate an automated deep learning model combined with a post-processing algorithm to segment six pulmonary anatomical regions in chest computed tomography (CT) images acquired during positron emission tomography/computed tomography (PET/CT) scans. The pulmonary regions have five pulmonary lobes and airway trees. Methods: Patients who underwent both PET/CT imaging with an extra chest CT scan were retrospectively enrolled. The pulmonary segmentation of six regions in CT was performed via a convolutional neural network (CNN) of DenseVNet architecture with some post-processing algorithms. Three evaluation metrics were used to assess the performance of this method, which combined deep learning and the post-processing method. The agreement between the combined model and ground truth segmentations in the test set was analyzed.Results: A total of 640 cases were enrolled. The combined model, which involved deep learning and postprocessing methods, had a higher performance than the single deep learning model. In the test set, the alllobes overall Dice coefficient, Hausdorff distance, and Jaccard coefficient were 0.972, 12.025 mm, and 0.948, respectively. The airway-tree Dice coefficient, Hausdorff distance, and Jaccard coefficient were 0.849, 32.076 mm, and 0.815, respectively. A good agreement was observed between our segmentation in every plot. Conclusions:The proposed model combining two methods can automatically segment five pulmonary lobes and airway trees on chest CT imaging in PET/CT. The performance of the combined model was higher than the single deep learning model in each region in the test set.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 92%

A deep learning-based post-processing method for automated pulmonary lobe and airway trees segmentation using chest CT images in PET/CT

Xing¹,

Zhang²,

Nie³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Although UNet is prevalent in the literature, different architectures originated from the field of natural imaging segmentation, such as SegNet, DeepLab and Region CNNs, are also employed. Some traditional techniques, such as SVM, K-Means and GMMs, are also sometimes involved [191][192][193][194][195][196][197]. For the input to these methods, most research uses patches of a pre-processed acquisition, normally consisting of HU intensity normalization.…”

Section: Deep Learningmentioning

confidence: 99%

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

et al. 2022

View full text Add to dashboard Cite

Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.

show abstract

“…In 2014, Mansoor et al presented a seminal approach that identified CTs with large volumetric differences between autosegmented lung and the thoracic cage and refined these lung segmentations using texture-based features. 7 Subsequent studies have approached VOI identification in myriad ways, such as threshold-based methodologies, 8 deep learning architectures, [9][10][11][12][13] anatomic or shape-prior models, 5,[14][15][16] and region-growing methods. 17,18 As methodologies to identify thoracic VOIs in pathologic lungs march forward, data to train and vet them must keep pace.…”

Section: Introductionmentioning

confidence: 99%

PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines

et al. 2020

View full text Add to dashboard Cite

This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non-small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive "NSCLC Radiomics" data collection. Four hundred and two thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy-eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert-vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10. 7937/tcia.2020.6c7y-gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungswhere current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from "NSCLC Radiomics," pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.

show abstract

High Throughput Lung and Lobar Segmentation by 2D and 3D CNN on Chest CT with Diffuse Lung Disease

Cited by 13 publications

References 20 publications

A deep learning-based post-processing method for automated pulmonary lobe and airway trees segmentation using chest CT images in PET/CT

A deep learning-based post-processing method for automated pulmonary lobe and airway trees segmentation using chest CT images in PET/CT

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines

Contact Info

Product

Resources

About