Autonomous Driving in the Lung using Deep Learning for Localization

Sganga, Jake; Eng, David; Graetzel, Chauncey; Camarillo, David B.

doi:10.48550/arxiv.1907.08136

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…In conclusion, we constructed a deep-learning model that can estimate the chronological age from images obtained by bronchoscopy, and the application of an XAI method to our model revealed the importance of bifurcation sites as age-dependent features in the human adult trachea and bronchi. Although deep-learning methodology has been applied to bronchoscopy for location guidance [ 20 , 21 ], our results suggest that an analysis based on deep-learning models can also be applied to biological evaluation of bronchoscopy images in the era of digital medicine. Further studies with various conditional datasets will be required to analyse the pathological significance of bronchial bifurcation sites.…”

Section: Discussionmentioning

confidence: 98%

“…Recent research on the analysis of images by artificial intelligence has progressed in the areas such as radiographs [ 17 ], pathological slides [ 18 ] and ophthalmic images [ 19 ]. Although some studies have developed anatomical interpretation models or bronchoscopic navigation systems that are aided by artificial intelligence [ 20 , 21 ], the analysis of bronchoscopic findings by artificial intelligence is still immature. As a next step, our study expands the possibility of deep learning in the analysis of bronchoscopy images by providing strong evidence that it can be used to extract age-dependent features from bronchoscopy images.…”

Section: Discussionmentioning

confidence: 99%

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Identification of age-dependent features of human bronchi using explainable artificial intelligence

Ikushima,

Usui

2023

ERJ Open Res

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BackgroundAging induces functional and structural alterations in organs and age-dependent parameters have been identified in various medical data sources. However, there is currently no specific clinical test to quantitatively evaluate age-related changes in bronchi. This study aimed to identify age-dependent bronchial features using explainable artificial intelligence (XAI) for bronchoscopy images.MethodsThe present study included 11 374 bronchoscopy images, divided into training and test datasets based on the time axis. We constructed convolutional neural network (CNN) models and evaluated these models using the correlation coefficient between the chronological age and the ‘bronchial age’ calculated from bronchoscopy images. We employed gradient-weighted class activation mapping (Grad-CAM) to identify age-dependent bronchial features that the model focuses on. We assessed the universality of our model by comparing the distribution of bronchial age for each respiratory disease or smoking history.ResultsWe constructed deep learning models using four representative CNN architectures to calculate bronchial age. Although the bronchial age showed a significant correlation with chronological age in each CNN architecture, EfficientNetB3 achieved the highest Pearson's correlation coefficient (0.9617). The application of Grad-CAM to the EfficientNetB3-based model revealed that the model predominantly attended to bronchial bifurcation sites, regardless of whether the model accurately predicted chronological age or exhibited discrepancies. There were no significant differences in the discrepancy between the bronchial age and chronological age among different respiratory diseases or according to smoking history.ConclusionBronchial bifurcation sites are universally important age-dependent features in bronchi, regardless of the type of respiratory disease or smoking history.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Identification of age-dependent features of human bronchi using explainable artificial intelligence

Ikushima,

Usui

2023

ERJ Open Res

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“…Recently, supervised data-intensive learning methods, such as neural networks (NNs) (Sganga et al, 2019b;Visentini-Scarzanella et al, 2017;Zhao et al, 2020;Shen et al, 2019), have been used for localization and tracking in bronchoscopies, providing better results than previous methods. Moreover, temporal learning techniques have recently been applied to other endoscopic modalites (Turan et al, 2017), but has not been appropriately tested in bronchoscopy.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we compare our model with current SoA for bronchoscopy tracking, OffsetNet (Sganga et al, 2019b), implementing it as described in the original publication. For such comparison we build on our best results in previous sections and choose as loss the combination L p M SE + L oCE , and as architecture, the convolutional recurrent network.…”

Section: Comparison To State-of-the-artmentioning

confidence: 99%

BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation

Borrego-Carazo¹,

Sánchez²,

Castells-Rufas³

et al. 2022

Preprint

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Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the videobronchoscopy to provide an effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera, or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.In the present paper we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standartized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state of the art camera pose estimation in videobronchoscopy.

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Autonomous Driving in the Lung using Deep Learning for Localization

Cited by 2 publications

References 19 publications

Identification of age-dependent features of human bronchi using explainable artificial intelligence

Identification of age-dependent features of human bronchi using explainable artificial intelligence

BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation

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