Your browser doesn't support javascript.
Learning label diffusion maps for semi-automatic segmentation of lung CT images with COVID-19
Neurocomputing ; 522:24-38, 2023.
Article in English | Academic Search Complete | ID: covidwho-2228400
ABSTRACT
[Display omitted] • A fully end-to-end deep learning approach for COVID-19 CT image segmentation. • The trained model induces the diffusion of seeds by taking as input a marked slice. • The method learns diffusion maps by predicting edge weights via deep contour learning, • The use of deep contour learning and seeded segmentation as an integrated method. Deep Learning (DL) has become one of the key approaches for dealing with many challenges in medical imaging, which includes lung segmentation in Computed Tomography (CT). The use of seeded segmentation methods is another effective approach to get accurate partitions from complex CT images, as they give users autonomy, flexibility and easy usability when selecting specific targets for measurement purposes or pharmaceutical interventions. In this paper, we combine the accuracy of deep contour leaning with the versatility of seeded segmentation to yield a semi-automatic framework for segmenting lung CT images from patients affected by COVID-19. More specifically, we design a DL-driven approach that learns label diffusion maps from a contour detection network integrated with a label propagation model, used to diffuse the seeds over the CT images. Moreover, the trained model induces the diffusion of the seeds by only taking as input a marked CT-scan, segmenting hundreds of CT slices in an unsupervised and recursive way. Another important trait of our framework is that it is capable of segmenting lung structures even in the lack of well-defined boundaries and regardless of the level of COVID-19 infection. The accuracy and effectiveness of our learned diffusion model are attested to by both qualitative as well as quantitative comparisons involving several user-steered segmentations methods and eight CT data sets containing different types of lesions caused by COVID-19. [ FROM AUTHOR]
Keywords

Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Type of study: Prognostic study / Qualitative research Language: English Journal: Neurocomputing Year: 2023 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Type of study: Prognostic study / Qualitative research Language: English Journal: Neurocomputing Year: 2023 Document Type: Article