Evaluation of automatic volumetry of honeycombing and ground glass opacity patterns in lung CT scans

Congress:

ECR 2022

Poster Number:

C-15193

Type:

Scientific Exhibit

Keywords:

Artificial Intelligence, Lung, CT, Computer Applications-General, Pathology

Authors:

M. M. Pieler, J. Hofmanninger, R. Donner, A. Sikka, E. Jiménez Arroyo, H. Prosch, R. Zhang, G. Langs, A. Makropoulos

DOI:

10.26044/ecr2022/C-15193

DOI-Link:

https://dx.doi.org/10.26044/ecr2022/C-15193

References

[1] Siddique, Nahian, et al. "U-Net and its variants for medical image segmentation: theory and applications." arXiv preprint arXiv:2011.01118 (2020).

[2] Sorensen, Th A. "A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons." Biol. Skar. 5 (1948): 1-34.

Fig 1: Overview of the pattern segmentation workflow.

Fig 2: Pattern segmentation examples for the honeycombing (HC) and ground glass...

Fig 3: Pearson product-moment correlation coefficients between the model predictions...

Fig 4: Volume comparison plots of honeycombing (HC) and ground glass opacity (GGO) of...