Dr. Martin Eichenlaub Profile

Dr. Martin Eichenlaub

at Universitätsklinikum Freiburg

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Proceedings Article | 3 April 2023 Presentation + Paper

Weakly supervised airway orifice segmentation in video bronchoscopy

Ron Keuth, Mattias Heinrich, Martin Eichenlaub, Marian Himstedt

Proceedings Volume 12464, 124640A (2023) https://doi.org/10.1117/12.2654229

KEYWORDS: Image segmentation, Bronchoscopy, In vivo imaging, Video, Data modeling, Binary data, Tissues, Semantics

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Video bronchoscopy is routinely conducted for biopsies of lung tissue suspected for cancer, monitoring of COPD patients and clarification of acute respiratory problems at intensive care units. The navigation within complex bronchial trees is particularly challenging and physically demanding, requiring long-term experiences of physicians. This paper addresses the automatic segmentation of bronchial orifices in bronchoscopy videos. Deep learning-based approaches to this task are currently hampered due to the lack of readily available ground truth segmentation data. Thus, we present a data-driven pipeline consisting of a k-means followed by a compact marker-based watershed algorithm which enables to generate airway instance segmentation maps from given depth images. In this way, these traditional algorithms serve as weak supervision for training a shallow CNN directly on RGB images solely based on a phantom dataset. We evaluate generalization capabilities of this model on two in-vivo datasets covering 250 frames on 21 different bronchoscopies. We demonstrate that the model is capable to transfer its knowledge to the unseen in-vivo domain, reaching an average error of 4.35 vs 7.98 pixels for detected centroids of airway segmentations by an image resolution of 128 × 128. Our quantitative and qualitative results indicate that in the context of video bronchoscopy, phantom data and weak supervision using non-learning-based approaches enable to gain a semantic understanding of airway structures

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