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arXiv Computer Vision

Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation

The paper introduces CoLeTra, a data augmentation technique designed to enhance topology accuracy in image segmentation of thin, tubular structures such as blood vessels. By artificially breaking these structures in training images while preserving the original labels, CoLeTra teaches models that continuous structures can appear disconnected, improving segmentation quality even when training labels are topologically inaccurate. Experiments across various architectures, loss functions, and datasets show that CoLeTra yields more topologically accurate segmentations and often boosts Dice and clDice scores, and the authors also release a dedicated dataset for topology-focused segmentation.

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Combinatorial Network-Based Manifold Topological Deep Learning for Image Analysis

Combinatorial Network-Based Manifold Topological Deep Learning (CNMTDL) is a new framework that represents medical images as discrete manifolds and decomposes them into three Hodge components. Features from these components are concatenated and fed into a combinatorial complex architecture, enabling higher‑order message passing between 0‑cells and 2‑cells via attention‑based blocks. CNMTDL was evaluated on six 2D and 3D datasets from the MedMNIST v2 benchmark, showing improved performance for medical image analysis.

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arXiv Machine Learning
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CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.

By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska
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arXiv AI
Oct 16

TRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation

TRACE is a topology‑aware, prediction‑adaptive objective for thin‑structure segmentation that uses a target skeleton to define required local connections. For each connection, it identifies the easiest valid reconnection under the current prediction and jointly supervises its bottleneck with the corresponding skeleton point, allowing supervision to adapt to the evolving prediction rather than remaining fixed. A topology‑aware skeleton carrier preserves both foreground and class‑specific topology, enabling reliable definition of these connections, and extensive experiments on six 2D and 3D benchmarks demonstrate improved connectivity and topology while maintaining strong segmentation accuracy with low training overhead.

By Junho Moon, Ikbeom Jang
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