Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Guidewire Segmentation in Robot-Assisted Cardiovascular Catheterization

• URL: http://arxiv.org/abs/2404.07594v1
• Date: Thu, 11 Apr 2024 09:23:44 GMT
• Title: Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Guidewire Segmentation in Robot-Assisted Cardiovascular Catheterization
• Authors: Olatunji Mumini Omisore, Toluwanimi Akinyemi, Anh Nguyen, Lei Wang,
• Abstract summary: We propose a weakly-supervised learning method with multi-lateral pseudo labeling for tool segmentation in cardiac angiograms. We trained the model end-to-end with weakly-annotated data obtained during robotic cardiac catheterization. Compared to three existing weakly-supervised methods, our approach yielded higher segmentation performance across three different cardiac angiogram data.
• Score: 4.894147633944561
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although robot-assisted cardiovascular catheterization is commonly performed for intervention of cardiovascular diseases, more studies are needed to support the procedure with automated tool segmentation. This can aid surgeons on tool tracking and visualization during intervention. Learning-based segmentation has recently offered state-of-the-art segmentation performances however, generating ground-truth signals for fully-supervised methods is labor-intensive and time consuming for the interventionists. In this study, a weakly-supervised learning method with multi-lateral pseudo labeling is proposed for tool segmentation in cardiac angiograms. The method includes a modified U-Net model with one encoder and multiple lateral-branched decoders that produce pseudo labels as supervision signals under different perturbation. The pseudo labels are self-generated through a mixed loss function and shared consistency in the decoders. We trained the model end-to-end with weakly-annotated data obtained during robotic cardiac catheterization. Experiments with the proposed model shows weakly annotated data has closer performance to when fully annotated data is used. Compared to three existing weakly-supervised methods, our approach yielded higher segmentation performance across three different cardiac angiogram data. With ablation study, we showed consistent performance under different parameters. Thus, we offer a less expensive method for real-time tool segmentation and tracking during robot-assisted cardiac catheterization.

Related papers

• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency [20.371144313009122]
  We propose a novel 3D framework with two consistency constraints for scribble-supervised multiple abdominal organ segmentation from CT. For more stable unsupervised learning, we use voxel-wise uncertainty to rectify the soft pseudo labels and then supervise the outputs of each decoder. Experiments on the public WORD dataset show that our method outperforms five existing scribble-supervised methods.
  arXiv  Detail & Related papers  (2023-09-18T12:50:58Z)
• Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries [29.201385352740555]
  We propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. A cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation.
  arXiv  Detail & Related papers  (2023-09-02T14:52:58Z)
• Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use? [6.144041824426555]
  We show that the proposed 3D nnU-Net outperforms alternative 2D and recurrent segmentation methods. Overall, the experimental results suggest that with sufficient training data, 3D nnU-Net could become the first automated tool to meet the standards of an everyday clinical device.
  arXiv  Detail & Related papers  (2023-05-03T09:38:52Z)
• Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network [13.331718119215436]
  This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly better performance.
  arXiv  Detail & Related papers  (2022-08-04T22:25:25Z)
• Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations [72.15956198507281]
  We propose PGV-CL, a novel pseudo-label guided cross-video contrast learning method to boost scene segmentation. We extensively evaluate our method on a public robotic surgery dataset EndoVis18 and a public cataract dataset CaDIS.
  arXiv  Detail & Related papers  (2022-07-20T05:42:19Z)
• Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data [49.419268399590045]
  Surgical instrument segmentation for robot-assisted surgery is needed for accurate instrument tracking and augmented reality overlays. Deep learning-based methods have shown state-of-the-art performance for surgical instrument segmentation, but their results depend on labelled data. In this paper, we demonstrate the limited generalizability of these methods on different datasets, including human robot-assisted surgeries.
  arXiv  Detail & Related papers  (2021-03-16T18:41:18Z)
• Reciprocal Landmark Detection and Tracking with Extremely Few Annotations [10.115679843920958]
  We propose a new end-to-end reciprocal detection and tracking model to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks.
  arXiv  Detail & Related papers  (2021-01-27T06:59:41Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video [64.44583693846751]
  We study the semi-supervised instrument segmentation from robotic surgical videos with sparse annotations. By exploiting generated data pairs, our framework can recover and even enhance temporal consistency of training sequences. Results show that our method outperforms the state-of-the-art semisupervised methods by a large margin.
  arXiv  Detail & Related papers  (2020-07-06T02:39:32Z)
• 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)

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.