Catheter Detection and Segmentation in X-ray Images via Multi-task Learning

• URL: http://arxiv.org/abs/2503.02717v1
• Date: Tue, 04 Mar 2025 15:32:32 GMT
• Title: Catheter Detection and Segmentation in X-ray Images via Multi-task Learning
• Authors: Lin Xi, Yingliang Ma, Ethan Koland, Sandra Howell, Aldo Rinaldi, Kawal S. Rhode,
• Abstract summary: We present a convolutional neural network model that integrates a resnet architecture with multiple prediction heads.<n>We also propose a multi-task learning strategy in which our model is trained to perform both accurate electrode detection and catheter segmentation simultaneously.
• Score: 0.7324614782534692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated detection and segmentation of surgical devices, such as catheters or wires, in X-ray fluoroscopic images have the potential to enhance image guidance in minimally invasive heart surgeries. In this paper, we present a convolutional neural network model that integrates a resnet architecture with multiple prediction heads to achieve real-time, accurate localization of electrodes on catheters and catheter segmentation in an end-to-end deep learning framework. We also propose a multi-task learning strategy in which our model is trained to perform both accurate electrode detection and catheter segmentation simultaneously. A key challenge with this approach is achieving optimal performance for both tasks. To address this, we introduce a novel multi-level dynamic resource prioritization method. This method dynamically adjusts sample and task weights during training to effectively prioritize more challenging tasks, where task difficulty is inversely proportional to performance and evolves throughout the training process. Experiments on both public and private datasets have demonstrated that the accuracy of our method surpasses the existing state-of-the-art methods in both single segmentation task and in the detection and segmentation multi-task. Our approach achieves a good trade-off between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.

Related papers

• Active learning using adaptable task-based prioritisation [7.0002224852386545]
  We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney.
  arXiv  Detail & Related papers  (2022-12-03T22:37:38Z)
• Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics [2.6105699925188257]
  Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies. We propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting.
  arXiv  Detail & Related papers  (2021-11-25T13:11:49Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation [62.49837463676111]
  We propose a novel scheme of episodic training with task augmentation on medical imaging classification. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting.
  arXiv  Detail & Related papers  (2021-06-13T03:56:59Z)
• Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images [15.7026400415269]
  We propose a novel learning-to-learn framework for landmark detection. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.
  arXiv  Detail & Related papers  (2021-05-19T13:39:18Z)
• Multi-task Supervised Learning via Cross-learning [102.64082402388192]
  We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
  arXiv  Detail & Related papers  (2020-10-24T21:35:57Z)
• Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification [62.10696018098057]
  We propose an easy-to-hard expert meta-training strategy to arrange the training tasks properly. A task hardness aware module is designed and integrated into the training procedure to estimate the hardness of a task. Experimental results on the miniImageNet and tieredImageNetSketch datasets show that the meta-learners can obtain better results with our expert training strategy.
  arXiv  Detail & Related papers  (2020-07-13T08:49:00Z)
• Unified Representation Learning for Efficient Medical Image Analysis [0.623075162128532]
  We propose a multi-task training approach for medical image analysis using a unified modality-specific feature representation (UMS-Rep) Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance.
  arXiv  Detail & Related papers  (2020-06-19T16:52:16Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)
• Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
  We propose a novel learning scheme for unpaired cross-modality image segmentation. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI. We have extensively validated our approach on two multi-class segmentation problems.
  arXiv  Detail & Related papers  (2020-01-06T20:03:17Z)

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.