Abstract
Most of the papers in the literature concerning the detection and evaluation of particular features from medical images (i.e., trauma, infections, etc.) are based on supervised techniques that always require large amounts of annotated data. On the contrary, in this work a completely unsupervised method is proposed, inspired by a previous 2D approach to segmentation, reformulated and extended to the case of 3D tomographic images.
The method uses a flooding algorithm for the extraction of regions in the lung area, followed by the association with the corresponding statistical model by parametric estimation. A maximum a posteriori estimate of the 3D label map is obtained by modeling the image as a Markov Random Field that includes space-contextual information by means of a simple local proximity model for inter-voxel dependence. At this level, the problem is reformulated in terms of energy minimization, which is addressed using the α - β swap graph-cut algorithm.
The proposed method was tested on 3D lung images in order to delineate infections from CT scans of the chest. A set of COVID-19 confirmed patients was then considered, and the results obtained showed accurate detection ability and remarkable robustness to the heterogeneity of CT scans considered.
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Iaconi, G. et al. (2023). Graph-Based Segmentation and Markov Random Field for Covid-19 Infection in Lung CT Volumes. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_5
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DOI: https://doi.org/10.1007/978-3-031-16281-7_5
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