6 papers
End-to-End Cell Detection via Instance-aware Graph Modeling
Ruochen Liu, Yalin Zheng, Jingxin Liu +5
Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To capture complex cellular interac…
LHMCF-Net: A Learned Hyperbolic Mean Curvature Flow Network for Medical Images Segmentation
Shuangshuang Duan, Chunlei He, Shoujun Huang +1
Motivated by the classical Chan-Vese model and the ability of deep priors to capture complex spatial structures, we develop a segmentation model that leverages learned hyperbolic m…
Hyperbolic mean curvature flow computed by physics-informed neural networks
Shuangshuang Duan, Chunlei He, Shoujun Huang +1
In this paper, we explore the evolution of plane curves and surfaces governed by the hyperbolic mean curvature flow. We propose a mesh-free approach based on the physics-informed n…
Information Entropy-Based Framework for Quantifying Tortuosity in Meibomian Gland Uneven Atrophy
Kesheng Wang, Xiaoyu Chen, Chunlei He +5
In the medical image analysis field, precise quantification of curve tortuosity plays a critical role in the auxiliary diagnosis and pathological assessment of various diseases. In…
Active Contour Models Driven by Hyperbolic Mean Curvature Flow for Image Segmentation
Saiyu Hu, Chunlei He, Jianfeng Zhang +2
Parabolic mean curvature flow-driven active contour models (PMCF-ACMs) are widely used for image segmentation, yet they suffer severe degradation under high-intensity noise because…
An edge detection-based deep learning approach for tear meniscus height measurement
Kesheng Wang, Kunhui Xu, Xiaoyu Chen +5
Automatic measurements of tear meniscus height (TMH) have been achieved by using deep learning techniques; however, annotation is significantly influenced by subjective factors and…