Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function

Lassen-Schmidt, Bianca; Hering, Alessa; Kraß, Stefan; Meine, Hans

doi:10.48550/arxiv.2006.00083

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…To validate the effectiveness of our proposed method, we compare the DAV-Net with previous state-of-the-art methods on the basis of the two datasets (LUNA16 test set and the in-house test set). Specifically, we evaluate the proposed DAV-Net with PTK [44], 3D U-Net [19,23,24], PDV-Net [25], and FRV-Net [22]. In our experiments, all methods adopt 3D CNNs under the same training set and hyperparameters, except for the PTK, which is a software suite for the analysis of 3D medical lung images in an unsupervised approach.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

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A dual‐attention V‐network for pulmonary lobe segmentation in CT scans

Zheng

Nie

Lin

et al. 2021

IET Image Processing

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The reliable and automatic segmentation of pulmonary lobes in computed tomography scans is an important pre-condition for the diagnosis, assessment, and treatment of lung diseases. However, due to the incomplete lobar structures and morphological changes caused by diseases, the lobe segmentation still encounters great challenges. Recently, convolution neural network has exerted a tremendous impact on medical image analysis. Nevertheless, the basic convolution operations mainly obtain local features that are insufficient for accurate lobe segmentation. The idea that the global features are equally crucial especially when lesions appear is considered. Here, a dual-attention V-network named DAV-Net for pulmonary lobe segmentation is proposed. First, a novel dual-attention module to capture global contextual information and model the semantic dependencies in spatial and channel dimensions is introduced. Second, a progressive output scheme is used to avoid the vanishing gradient phenomenon and obtain relatively effective features in hidden layers. Finally, an improved combo loss is devised to address input and output lobe imbalance problem during training and inference. In the evaluation using the LUNA16 dataset and our in-house dataset, the proposed DAV-Net obtains Dice similarity coefficients of 0.947 and 0.934, respectively; these values are superior to those obtained by existing methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Ferreira et al [22] presented a model of lobe segmentation based on V-Net called FRV-Net, which employs additional regularization techniques to mitigate overfitting. Park et al [23] and Lassen et al [24] later proposed a lung lobe segmentation strategy that is based on 3D U-Net. Imran et al [25] introduced a progressive dense V-Net.…”

Section: Introductionmentioning

confidence: 99%

A dual‐attention V‐network for pulmonary lobe segmentation in CT scans

Zheng

Nie

Lin

et al. 2021

IET Image Processing

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“…The authors give an overview of the deep learning research literature with regard to lung image analysis applications. Lassen-Schmidt et al ( 46 ) present the automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function.…”

Section: Current Work and Outlookmentioning

confidence: 99%

Computer-assisted image-based risk analysis and planning in lung surgery - a review

2022

Self Cite

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In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

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“…These approaches yielded good results without the need for domain expertise and modeling. Various ideas were explored to improve performance such as multitasking [11,12], dense networks [13], leveraging global geometric features as additional inputs [14], cascaded networks for global and local features [15], holistically nested network [16,17] and advanced loss functions to tackle class imbalance issue [18]. However, these techniques offer only minor improvements, mainly due to limitations in the datasets used for training, which have been shown to be the main source of errors [19].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Techniques to Improve Generalization and Robustness of U-Net Based Networks for Pulmonary Lobe Segmentation

Dadras,

Jaziri,

Frodl

et al. 2023

Bioengineering

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Lung lobe segmentation in chest CT is relevant to a wide range of clinical applications. However, existing segmentation pipelines often exhibit vulnerabilities and performance degradations when applied to external datasets. This is usually attributed to the size of the available dataset or model. We show that it is possible to enhance generalizability without huge resources by carefully curating the dataset and combining machine learning with medical expertise. Multiple machine learning techniques (self-supervision (SSL), attention (A), and data augmentation (DA)) are used to train a fast and fully-automated lung lobe segmentation model based on 2D U-Net. Our study involved evaluating these techniques on a diverse dataset collected under the RACOON project, encompassing 100 CT chest scans from patients with bacterial, viral, or SARS-CoV2 infections. We compare our model to a baseline U-Net trained on the same dataset. Our approach significantly improved segmentation accuracy (Dice score of 92.8% vs. 82.3%, p < 0.001). Moreover, our model achieved state-of-the-art performance (Dice score of 92.8% vs. 90.8% for the literature’s state-of-the-art, p = 0.102) with reduced training examples (69 vs. 231 CT Scans). Among the techniques, data augmentation with expert knowledge displayed the most significant impact, enhancing the Dice score by +0.056. Notably, these enhancements are not limited to lobe segmentation but can be seamlessly integrated into various medical imaging segmentation tasks, demonstrating their versatility and potential for broader applications.

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Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function

Cited by 3 publications

References 6 publications

A dual‐attention V‐network for pulmonary lobe segmentation in CT scans

A dual‐attention V‐network for pulmonary lobe segmentation in CT scans

Computer-assisted image-based risk analysis and planning in lung surgery - a review

Lightweight Techniques to Improve Generalization and Robustness of U-Net Based Networks for Pulmonary Lobe Segmentation

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