Leveraging Persistence Image to Enhance Robustness and Performance in Curvilinear Structure Segmentation

• URL: http://arxiv.org/abs/2601.18045v1
• Date: Sun, 25 Jan 2026 23:51:45 GMT
• Title: Leveraging Persistence Image to Enhance Robustness and Performance in Curvilinear Structure Segmentation
• Authors: Zhuangzhi Gao, Feixiang Zhou, He Zhao, Xiuju Chen, Xiaoxin Li, Qinkai Yu, Yitian Zhao, Alena Shantsila, Gregory Y. H. Lip, Eduard Shantsila, Yalin Zheng,
• Abstract summary: PIs-Regressor learns persistence image (PI) - finite, differentiable representations of topological features - directly from data.<n>Unlike existing methods that depend heavily on handcrafted loss functions, our approach directly incorporates topological information into the network structure.<n>Our approach on three curvilinear benchmarks demonstrate state-of-the-art performance in both pixel-level accuracy and topological fidelity.
• Score: 17.808385571339624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting curvilinear structures in medical images is essential for analyzing morphological patterns in clinical applications. Integrating topological properties, such as connectivity, improves segmentation accuracy and consistency. However, extracting and embedding such properties - especially from Persistence Diagrams (PD) - is challenging due to their non-differentiability and computational cost. Existing approaches mostly encode topology through handcrafted loss functions, which generalize poorly across tasks. In this paper, we propose PIs-Regressor, a simple yet effective module that learns persistence image (PI) - finite, differentiable representations of topological features - directly from data. Together with Topology SegNet, which fuses these features in both downsampling and upsampling stages, our framework integrates topology into the network architecture itself rather than auxiliary losses. Unlike existing methods that depend heavily on handcrafted loss functions, our approach directly incorporates topological information into the network structure, leading to more robust segmentation. Our design is flexible and can be seamlessly combined with other topology-based methods to further enhance segmentation performance. Experimental results show that integrating topological features enhances model robustness, effectively handling challenges like overexposure and blurring in medical imaging. Our approach on three curvilinear benchmarks demonstrate state-of-the-art performance in both pixel-level accuracy and topological fidelity.

Related papers

• Bridging Structure and Appearance: Topological Features for Robust Self-Supervised Segmentation [8.584363058858935]
  Self-supervised semantic segmentation methods often fail when faced with appearance ambiguities.<n>We argue that this is due to an over-reliance on unstable, appearance-based features such as shadows, glare, and local textures.<n>We propose textbfGASeg, a novel framework that bridges appearance and geometry by leveraging stable topological information.
  arXiv  Detail & Related papers  (2025-12-30T05:34:28Z)
• SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning [2.3436632098950456]
  Key challenge is ensuring topological correctness while maintaining computational efficiency.<n>We propose textbfSDF-TopoNet, an improved topology-aware segmentation framework.<n>We show that SDF-TopoNet outperforms existing methods in both topological accuracy and quantitative segmentation metrics.
  arXiv  Detail & Related papers  (2025-03-14T23:54:38Z)
• A Graph-Based Framework for Interpretable Whole Slide Image Analysis [86.37618055724441]
  We develop a framework that transforms whole-slide images into biologically-informed graph representations.<n>Our approach builds graph nodes from tissue regions that respect natural structures, not arbitrary grids.<n>We demonstrate strong performance on challenging cancer staging and survival prediction tasks.
  arXiv  Detail & Related papers  (2025-03-14T20:15:04Z)
• Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structures [38.20599800950335]
  We introduce Conformable Convolution, a novel convolutional layer designed to explicitly enforce topological consistency.<n>Topological Posterior Generator (TPG) module identifies key topological features and guides the convolutional layers.<n>We showcase the effectiveness of our framework in the segmentation task, where preserving the interconnectedness of structures is critical.
  arXiv  Detail & Related papers  (2024-12-29T22:41:33Z)
• Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation [78.54656076915565]
  Topological correctness plays a critical role in many image segmentation tasks.<n>Most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy.<n>We propose a novel, graph-based framework for topologically accurate image segmentation.
  arXiv  Detail & Related papers  (2024-11-05T16:20:14Z)
• TopoFR: A Closer Look at Topology Alignment on Face Recognition [58.45515807380505]
  We propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE.<n> PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model.<n> Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T14:58:30Z)
• Graph Vertex Embeddings: Distance, Regularization and Community Detection [0.0]
  Graph embeddings have emerged as a powerful tool for representing complex network structures in a low-dimensional space. We present a family of flexible distance functions that faithfully capture the topological distance between different vertices. We evaluate the effectiveness of our proposed embedding constructions by performing community detection on a host of benchmark datasets.
  arXiv  Detail & Related papers  (2024-04-09T09:03:53Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Localized Persistent Homologies for more Effective Deep Learning [60.78456721890412]
  We introduce an approach that relies on a new filtration function to account for location during network training. We demonstrate experimentally on 2D images of roads and 3D image stacks of neuronal processes that networks trained in this manner are better at recovering the topology of the curvilinear structures they extract.
  arXiv  Detail & Related papers  (2021-10-12T19:28:39Z)
• InverseForm: A Loss Function for Structured Boundary-Aware Segmentation [80.39674800972182]
  We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-04-06T18:52:45Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.