Hongbin Tu

Yuanyuan Peng, Lin Pan, Pengpeng Luan et al.

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.

Yuanyuan Peng, Pengpeng Luan, Hongbin Tu et al.

Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung diseases. In CT images, pulmonary fissure segmentation is a formidable mission due to various of factors. To address the challenge, an useful approach based on ODoS filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by merging the orientation information and magnitude information to highlight structure features for fissure enhancement, which can effectively distinguish between pulmonary fissures and clutters. Motivated by the fact that pulmonary fissures appear as linear structures in 2D space and planar structures in 3D space in orientation field, an orientation curvature criterion and an orientation partition scheme are fused to separate fissure patches and other structures in different orientation partition, which can suppress parts of clutters. Considering the shape difference between pulmonary fissures and tubular structures in magnitude field, a shape measure approach and a 3D skeletonization model are combined to segment pulmonary fissures for clutters removal. When applying our scheme to 55 chest CT scans which acquired from a publicly available LOLA11 datasets, the median F1-score, False Discovery Rate (FDR), and False Negative Rate (FNR) respectively are 0.896, 0.109, and 0.100, which indicates that the presented method has a satisfactory pulmonary fissure segmentation performance.

Yuanyuan Peng, Zixu Zhang, Hongbin Tu et al.

Background: The 2019 novel coronavirus disease (COVID-19) has been spread widely in the world, causing a huge threat to people's living environment. Objective: Under computed tomography (CT) imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Considering the fact that a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtained, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract COVID-19 lesion features effectively, which may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results shown that the proposed mehtod has a perfect performance in COVID-19 lesion segmentation.