Pengpeng Luan

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, Zixu Zhang

Accurately segmenting blood vessels in retinal fundus images is crucial in the early screening, diagnosing, and evaluating some ocular diseases, yet it poses a nontrivial uncertainty for the segmentation task due to various factors such as significant light variations, uneven curvilinear structures, and non-uniform contrast. As a result, a multiple attention-guided fusion network (MAF-Net) is proposed to accurately detect blood vessels in retinal fundus images. Currently, traditional UNet-based models may lose partial information due to explicitly modeling long-distance dependencies, which may lead to unsatisfactory results. To enrich contextual information for the loss of scene information compensation, an attention fusion mechanism that combines the channel attention with spatial attention mechanisms constructed by Transformer is employed to extract various features of blood vessels from retinal fundus images. Subsequently, a unique spatial attention mechanism is applied in the skip connection to filter out redundant information and noise from low-level features, thus enabling better integration with high-level features. In addition, a DropOut layer is employed to randomly discard some neurons, which can prevent overfitting of the deep learning network and improve its generalization performance. Experimental results were verified in public datasets DRIVE, STARE and CHASEDB1 with F1 scores of 0.818, 0.836 and 0.811, and Acc values of 0.968, 0.973 and 0.973, respectively. Both visual inspection and quantitative evaluation demonstrate that our method produces satisfactory results compared to 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.