Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

Peng, Yuanyuan; Zhong, Huihuang; Xu, Zheng; Tu, Hongbin; Lan, Peng

doi:10.1155/2021/5588629

Cited by 9 publications

(8 citation statements)

References 44 publications

(100 reference statements)

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“…Peng et al identified the fissure region of interest using lung anatomy prior knowledge and then isolated the plate-like structures from clutters utilizing an oriented derivative of stick filter for lobar fissure verification. Finally, to segment lung lobes, they completed the incomplete fissure surface employing a surface fitting model [ 66 ]. Qi et al noted that one reason for incomplete localization regions was neglecting the anatomical region relationships within each image and the inter-image relationships [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

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Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

et al. 2022

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Background. There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications. Methods. We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis. Results. We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability. Conclusions. We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.

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Section: Resultsmentioning

confidence: 99%

“…Existing work like Peng et al heavily depends on other tasks like airway segmentation. However, the segmentation is an arduous task as it is highly sensitive to the image quality [ 66 ]. They segmented pulmonary fissures using lung anatomy knowledge, which is time-consuming.…”

Section: Discussionmentioning

confidence: 99%

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

et al. 2022

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“…The human lungs are naturally divided into five separate functional compartments called lobes. The physical boundaries between lung lobes are known as lobar fissures 1 . Anatomically, the left lung is subdivided into two lobes (upper and lower lobes), whereas the right lung is subdivided into three lobes (upper, middle, and lower lobes), 2 as described in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Lung lobe segmentation in computed tomography images based on multi‐feature fusion and ensemble learning framework

Peng

Zhang

2023

Int J Imaging Syst Tech

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Lung lobe segmentation in computed tomography (CT) images can be regarded as essential supporting information for the diagnosis and treatment of lung diseases, yet it is a challenging uncertainty for the complex segmentation task due to the diverse structures like indistinguishable pulmonary arteries and veins, unpredictable pathological deformation and blurring pulmonary fissures. To circumvent these challenges, we present a productive deep learning network based on multi‐feature fusion and ensemble learning approach to highlight pulmonary fissure representation and suppress other structures for lung lobe segmentation in CT images. Considering that pulmonary fissures are the physical boundaries of lung lobes, a multi‐feature fusion approach is presented to integrate original images, enhanced fissure magnitude images, and enhanced fissure orientation images to highlight shape representation for semantic segmentation. In addition, a deep‐supervised ensemble learning network is employed to combine multiple inducers for lung lobe segmentation improvement. The performance of the proposed deep learning model is validated in experiments with an available dataset, it acquired a high IoU value and a low Hausdorff distance compared with manual references. Experimental results demonstrate that the proposed computational model based on multi‐feature fusion and deep‐supervised ensemble learning framework has an improved predictive performance than many state‐of‐the‐art deep neural networks in lung lobe segmentation.

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“…Traditional methods achieve the purpose of different semantic segmentation tasks by extracting features of the target objects (10)(11)(12), but the segmentation performance was not good enough. To overcome the problem, various deep learning frameworks have been proposed to effectively segment the target objects (13).…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

Peng

Zhang

2022

Front. Med.

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Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people.Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images.Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation.Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604.Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

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Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

Cited by 9 publications

References 44 publications

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

Lung lobe segmentation in computed tomography images based on multi‐feature fusion and ensemble learning framework

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

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