Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation

• URL: http://arxiv.org/abs/2505.08525v2
• Date: Fri, 23 May 2025 18:28:14 GMT
• Title: Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation
• Abstract summary: This paper introduces a dynamic snake upsampling operator and a boundary-skeleton weighted loss tailored for topological tubular structures.<n> Experiments across various domain datasets and backbone networks show that this plug-and-play dynamic snake upsampling operator and boundary-skeleton weighted loss boost both pixel-wise segmentation accuracy and topological consistency of results.
• Score: 5.949900966039577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate segmentation of tubular topological structures (e.g., fissures and vasculature) is critical in various fields to guarantee dependable downstream quantitative analysis and modeling. However, in dense prediction tasks such as semantic segmentation and super-resolution, conventional upsampling operators cannot accommodate the slenderness of tubular structures and the curvature of morphology. This paper introduces a dynamic snake upsampling operators and a boundary-skeleton weighted loss tailored for topological tubular structures. Specifically, we design a snake upsampling operators based on an adaptive sampling domain, which dynamically adjusts the sampling stride according to the feature map and selects a set of subpixel sampling points along the serpentine path, enabling more accurate subpixel-level feature recovery for tubular structures. Meanwhile, we propose a skeleton-to-boundary increasing weighted loss that trades off main body and boundary weight allocation based on mask class ratio and distance field, preserving main body overlap while enhancing focus on target topological continuity and boundary alignment precision. Experiments across various domain datasets and backbone networks show that this plug-and-play dynamic snake upsampling operator and boundary-skeleton weighted loss boost both pixel-wise segmentation accuracy and topological consistency of results.

Related papers

• Towards Voxel Spacing Consistency for Medical Image Segmentation [56.992050146446495]
  We study the correlation between image resampling and segmentation, and propose Consispace, a semantic-aware resampling framework.<n> Consispace achieves consistent voxel spacing in the axial direction while preserving anatomical and semantic consistency.
  arXiv  Detail & Related papers  (2026-06-30T15:42:23Z)
• CurvSegFlow: Time-Conditioned Flow Matching for Robust Segmentation of Curvilinear Structures in Noisy Biomedical Images [33.96173028868693]
  We propose CurvSegFlow, a segmentation framework based on time-conditioned flow matching.<n>The proposed model combines a U-Net backbone with triple-term loss function and temporal embeddings to guide the refinement process.<n>CurvSegFlow is evaluated on multiple synthetic and real microtubule datasets, as well as on public benchmarks of retinal vessels, corneal nerves and coronary arteries.
  arXiv  Detail & Related papers  (2026-06-19T17:12:49Z)
• Topological Flow Matching [56.16372813551555]
  Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance.<n>We introduce topological flow matching, a topology-aware generalization of flow matching.<n>We demonstrate its effectiveness on diverse structured datasets, including brain fMRIs, ocean currents, seismic events, and traffic flows.
  arXiv  Detail & Related papers  (2026-06-14T16:29:33Z)
• MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
  Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
  arXiv  Detail & Related papers  (2026-05-18T01:52:12Z)
• A Mean Curvature Approach to Boundary Detection: Geometric Insights for Unsupervised Learning [52.452902154360565]
  We introduce Mean Curvature Boundary Points (MCBP), a novel geometric framework grounded in Geometric Machine Learning.<n>MCBP explicitly models the intrinsic curvature of the data manifold and computes pointwise mean curvature without requiring principled manifold parametrization.<n>Experiments on synthetic and real-world datasets demonstrate that MCBP consistently improves clustering performance.
  arXiv  Detail & Related papers  (2026-05-05T20:19:09Z)
• Hierarchical Mesh Transformers with Topology-Guided Pretraining for Morphometric Analysis of Brain Structures [7.663406601980095]
  We introduce a hierarchical transformer framework for heterogeneous mesh analysis.<n>A feature projection module maps variable-length per-vertex clinical descriptors into the spatial hierarchy.<n>We validate our approach on Alzheimer's disease classification and amyloid burden prediction using volumetric brain meshes from ADNI, as well as focal cortical dysplasia detection on cortical surface meshes from the MELD dataset.
  arXiv  Detail & Related papers  (2026-04-06T22:27:36Z)
• Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers [46.87466345672103]
  Boundary representation (B-rep) is the industry standard for computer-aided design (CAD)<n>While deep learning shows promise in processing B-rep models, existing methods suffer from a representation gap.<n>We introduce Brep2Shape, a novel self-supervised pre-training method designed to align abstract boundary representations with intuitive shape representations.
  arXiv  Detail & Related papers  (2026-02-07T08:00:47Z)
• Leveraging Persistence Image to Enhance Robustness and Performance in Curvilinear Structure Segmentation [17.808385571339624]
  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.
  arXiv  Detail & Related papers  (2026-01-25T23:51:45Z)
• A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences [6.154694408048338]
  We propose a novel textitPhysics-Informed Loss (PIL) that models the interaction between the predicted and ground-truth boundaries.<n>PIL consistently outperforms conventional loss functions such as Cross-Entropy, Dice, Active Contour, and Surface losses.
  arXiv  Detail & Related papers  (2025-11-25T17:08:14Z)
• VIKING: Deep variational inference with stochastic projections [48.946143517489496]
  Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks.<n>We propose a simple variational family that considers two independent linear subspaces of the parameter space.<n>This allows us to build a fully-correlated approximate posterior reflecting the overparametrization.
  arXiv  Detail & Related papers  (2025-10-27T15:38:35Z)
• Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures [11.265651259708497]
  Deep learning methods enable automated shape modeling by prioritizing volumetric overlap accuracy.<n>The inherent complexity of fine-grained semantic tubular shapes is not fully emphasized by overlap accuracy, resulting in reduced topological preservation.<n>We propose the Shapeaware Sampling (SAS), which optimize patchsize allocation for online sampling and extracts a topology-preserved skeletal representation for the objective function.
  arXiv  Detail & Related papers  (2025-06-14T08:20:08Z)
• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors.<n>Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images.<n>Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability of Decoder-Only Transformer Models [0.0]
  Recent evidence from miniature neural networks suggests that the boundary separating these outcomes displays fractal characteristics.<n>This study extends them to medium-sized, decoder-only transformer architectures by employing a more consistent convergence measure.<n>The results show that the trainability frontier is not a simple threshold; rather, it forms a self-similar yet seemingly random structure at multiple scales.
  arXiv  Detail & Related papers  (2025-01-08T05:24:11Z)
• Network scaling and scale-driven loss balancing for intelligent poroelastography [2.665036498336221]
  A deep learning framework is developed for multiscale characterization of poroelastic media from full waveform data. Two major challenges impede direct application of existing state-of-the-art techniques for this purpose. We propose the idea of emphnetwork scaling where the neural property maps are constructed by unit shape functions composed into a scaling layer.
  arXiv  Detail & Related papers  (2024-10-27T23:06:29Z)
• Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods [7.646983689651424]
  Topological consistency plays a crucial role in the task of boundary segmentation for reticular images. We propose the Skea-Topo Aware loss, which is a novel loss function that takes into account the shape of each object and topological significance of the pixels. Experiments prove that our method improves topological consistency by up to 7 points in VI compared to 13 state-of-art methods.
  arXiv  Detail & Related papers  (2024-04-29T09:27:31Z)
• Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning [77.1421343649344]
  We propose a generalization of Transformers towards operating entirely on the product of constant curvature spaces. We also provide a kernelized approach to non-Euclidean attention, which enables our model to run in time and memory cost linear to the number of nodes and edges.
  arXiv  Detail & Related papers  (2023-09-08T02:44:37Z)
• Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation [12.081234339680456]
  We use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint. Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy and continuity on the tubular structure segmentation task compared with several methods.
  arXiv  Detail & Related papers  (2023-07-17T10:55:58Z)
• Recurrent Generic Contour-based Instance Segmentation with Progressive Learning [111.31166268300817]
  We propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance.
  arXiv  Detail & Related papers  (2023-01-21T05:34:29Z)
• Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks [63.596602299263935]
  We present a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates. We show that our method outperforms state-of-the-art methods in terms of prediction accuracy of mesh deformations by about 20% in RMSE and 10% in Hausdorff distance and STED.
  arXiv  Detail & Related papers  (2022-05-03T07:54:39Z)
• 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)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours [12.791313859673187]
  "ProAlignNet" accounts for large scale misalignments and complex transformations between the contour shapes. It learns by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric. In two real-world applications, the proposed models consistently perform superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-23T14:56:14Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)

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.