Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image.

ABSTRACT: Purpose: Segmentation of lung nodules in chest CT images is essential for image-driven lung cancer diagnosis and follow-up treatment planning. Manual segmentation of lung nodules is subjective because the approach depends on the knowledge and experience of the specialist. We proposed a multiscale fully convolutional three-dimensional UNet (MF-3D UNet) model for automatic segmentation of lung nodules in CT images. Approach: The proposed model employs two strategies, fusion of multiscale features with Maxout aggregation and trainable downsampling, to improve the performance of nodule segmentation in 3D CT images. The fusion of multiscale (fine and coarse) features with the Maxout function allows the model to retain the most important features while suppressing the low-contribut

SUBMITTER: Agnes SA 

PROVIDER: S-EPMC9093212 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature