# Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation

arXiv: https://arxiv.org/abs/2005.13366 · HTML: https://mlsanity.com/paper/2005.13366/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 14 of 14)

- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation](https://mlsanity.com/paper/2208.05669/) (2022)
- [Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites](https://mlsanity.com/paper/2206.06813/) (2022)
- [Robust Implementation of Foreground Extraction and Vessel Segmentation for X-ray Coronary Angiography Image Sequence](https://mlsanity.com/paper/2209.07237/) (2022)
- [Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation](https://mlsanity.com/paper/2106.11447/) (2021)
- [SS-CADA: A Semi-Supervised Cross-Anatomy Domain Adaptation for Coronary Artery Segmentation](https://mlsanity.com/paper/2105.02674/) (2021)
- [Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning](https://mlsanity.com/paper/2307.04472/) (2023)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [One-shot Weakly-Supervised Segmentation in Medical Images](https://mlsanity.com/paper/2111.10773/) (2021)
- [Accelerating the creation of instance segmentation training sets through bounding box annotation](https://mlsanity.com/paper/2205.11563/) (2022)
- [Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery](https://mlsanity.com/paper/2310.19109/) (2023)
- [Weakly-semi-supervised object detection in remotely sensed imagery](https://mlsanity.com/paper/2311.17449/) (2023)
- [Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs](https://mlsanity.com/paper/2402.03492/) (2024)
- [SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations](https://mlsanity.com/paper/2609.23019/) (2026)

## Abstract (arXiv)

The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in a large number of training images, which is extremely labor-intensive especially for complex coronary trees. To alleviate the burden on the annotator, we propose a novel weakly supervised training framework that learns from noisy pseudo labels generated from automatic vessel enhancement, rather than accurate labels obtained by fully manual annotation. A typical self-paced learning scheme is used to make the training process robust against label noise while challenged by the systematic biases in pseudo labels, thus leading to the decreased performance of CNNs at test time. To solve this problem, we propose an annotation-refining self-paced learning framework (AR-SPL) to correct the potential errors using suggestive annotation. An elaborate model-vesselness uncertainty estimation is also proposed to enable the minimal annotation cost for suggestive annotation, based on not only the CNNs in training but also the geometric features of coronary arteries derived directly from raw data. Experiments show that our proposed framework achieves 1) comparable accuracy to fully supervised learning, which also significantly outperforms other weakly supervised learning frameworks; 2) largely reduced annotation cost, i.e., 75.18% of annotation time is saved, and only 3.46% of image regions are required to be annotated; and 3) an efficient intervention process, leading to superior performance with even fewer manual interactions.