Determining Alveolar Dynamics by Automatic Tracing of Area Changes Within Microscopy Videos

Schwenninger, David; Möller, Knut; Lui, Hoi-Shun; Runck, Hanna; Stahl, C; Schumann, Stefan; Guttmann, J.

doi:10.1109/icbbe.2008.916

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…Fig 4). Thus, this method allows the segmentation of an object in a videosequence, while in the prior method (frame-by-frame segmentation by a GVF Snake [11]) an object in a set of M images is treated like M independent objects each in an individual frame.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the subpleural alveolus shows cyclical changes in form and size. The Snakes was initialized by applying the 2D-GVF Snake for every single frame of the video individually [11]. Figure 4 shows the area size of the segments from the initializing frame-by-frame method and the area size of the same segments that were processed by the simplified 3D-Snake method.…”

Section: Resultsmentioning

confidence: 99%

“…Similar to Fint, the smoothing-effect is, for F3D, achieved by calculating the 2nd order difference quotient over the corresponding coordinates -only this time over Δt instead of Δd: (10) This calculation assumes that two coordinates with the same index k at different time-points t can simply be connected to form the 3D object.…”

Section: Methodsmentioning

confidence: 99%

“…computer tomography or magnet resonance tomography). The algorithm is used to improve the result of a prior frame-by-frame segmentation [11] by utilizing the segmentation-results of neighbouring video-frames to reduce the influence of instable image data (e.g. edges that are not present in all frames of the video) to the segmentation result.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm was developed to improve the segmentation of alveoli in videos of subpleural alveoli [10]. In this case, the video frames over time form a stack of images -similar to a stack of cross sectional images produced by a medical imaging device (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fast Parametric Active Contour to Improve 3D Segmentation of Tubular Structures

Schwenninger

Guttmann

Möller

2009

2009 3rd International Conference on Bioinformatics and Biomedical Engineering

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Segmentation of three dimensional (3D) image data is an important task in medical imaging. The segmentation of tubular structures like e.g. colons, blood vessels or bronchioli is, thereby, an important subtask. Qualitative segmentation of subpleural alveoli in microscopic videos (time as the third dimension) is the objective of the present study. The proposed approach attempts to improve a given segmentation that was obtained by a two dimensional parametric active contour algorithm with an additional smoothness criterion for the third dimension. Compared to 2D segmentation alone, the developed approach decreased the high-frequency noise and resolved problems due to the ambiguity of image-features.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast Parametric Active Contour to Improve 3D Segmentation of Tubular Structures

Schwenninger

Guttmann

Möller

2009

2009 3rd International Conference on Bioinformatics and Biomedical Engineering

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From morphological heterogeneity at alveolar level to the overall mechanical lung behavior: an in vivo microscopic imaging study

et al. 2014

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In six male anesthetized, tracheotomized, and mechanically ventilated rabbits, we imaged subpleural alveoli under microscopic view (60×) through a “pleural window” obtained by stripping the endothoracic fascia and leaving the parietal pleura intact. Three different imaging scale levels were identified for the analysis on increasing stepwise local distending pressure (Pld) up to 16.5 cmH2O: alveoli, alveolar cluster, and whole image field. Alveolar profiles were manually traced, clusters of alveoli of similar size were identified through a contiguity‐constrained hierarchical agglomerative clustering analysis and alveolar surface density (ASD) was estimated as the percentage of air on the whole image field. Alveolar area distributions were remarkably right‐skewed and showed an increase in median value with a large topology‐independent heterogeneity on increasing Pld. Modeling of alveolar area distributions on increasing Pld led to hypothesize that absolute alveolar compliance (change in surface area over change in Pld) increases fairly linearly with increasing initial alveolar size, the corollary of this assumption being a constant specific compliance. Clusters were reciprocally interweaved due to their highly variable complex shapes. ASD was found to increase with a small coefficient of variation (CV <25%) with increasing Pld. The CV of lung volume at each transpulmonary pressure was further decreased (about 6%). The results of the study suggest that the considerable heterogeneity of alveolar size and of the corresponding alveolar mechanical behavior are homogenously distributed, resulting in a substantially homogenous mechanical behavior of lung units and whole organ.

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