Aug 7, 2020 · In this paper, we proposed a novel segmentation algorithm based on random forest (RF), deep convolutional network, and multi-scale superpixels ...

Experiments show that our algorithm can achieve a high segmentation accuracy with an average DSC of 96.45% and PPV of 95.07%. Compared with several existing ...

A pathological thoracic CT image is first segmented based on multi-scale superpixels, and deep features, texture, and intensity features extracted from ...

Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels. https://doi.org/10.1007/s11063-020-10330-8 ·.

The algorithm carries a first step of lung region extraction and a second step of lung nodule segmentation. By combining texture information, the improved ...

Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels · Caixia Liu · Ruibin Zhao · Wangli Xie · Mingyong Pang.

Jul 15, 2021 · Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases.

The quantitative results show that the pathological lung segmentation method improves on current standards because of its high sensitivity and specificity ...

Feb 23, 2021 · The authors propose a novel algorithm to segment lungs from CT images in an automatic and accurate fashion.

In this paper, we present a novel algorithm to segment lungs from CT images in an accurate and automatical fashion.