3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations

• URL: http://arxiv.org/abs/2302.03299v1
• Date: Tue, 7 Feb 2023 07:26:00 GMT
• Title: 3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations
• Authors: Huai Chen, Xiuying Wang, Lisheng Wang
• Abstract summary: Supervised deep learning has demonstrated its superior capacity in automatic 3D vessel segmentation. The reliance on expensive 3D manual annotations and limited capacity for annotation reuse hinder the clinical applications of supervised models. This paper proposes a novel 3D shape-guided local discrimination model for 3D vascular segmentation under limited guidance from public 2D vessel annotations.
• Score: 3.6314292723682784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Delineating 3D blood vessels is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Supervised deep learning has demonstrated its superior capacity in automatic 3D vessel segmentation. However, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse hinder the clinical applications of supervised models. To avoid the repetitive and laborious annotating and make full use of existing vascular annotations, this paper proposes a novel 3D shape-guided local discrimination model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images; secondly, based on the similarity of the tree-shaped morphology between 2D and 3D vessels, the Crop-and-Overlap strategy is presented to generate reference masks from 2D structure-agnostic vessel annotations, which are fit for varied vascular structures, and the adversarial loss is introduced to guide the tree-shaped morphology of 3D vessels; thirdly, the temporal consistency loss is proposed to foster the training stability and keep the model updated smoothly. To further enhance the model's robustness and reliability, the orientation-invariant CNN module and Reliability-Refinement algorithm are presented. Experimental results from the public 3D cerebrovascular and 3D arterial tree datasets demonstrate that our model achieves comparable effectiveness against nine supervised models.

Related papers

• 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning [79.60829508459753]
  Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
  arXiv  Detail & Related papers  (2024-05-17T11:23:33Z)
• 3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection [33.34240545722551]
  Vascular structure segmentation plays a crucial role in medical analysis and clinical applications. Existing weakly supervised methods have exhibited suboptimal performance when handling sparse vascular structure. Here, we employ maximum intensity projection (MIP) to decrease the dimensionality of 3D volume to 2D image for efficient annotation. We introduce a weakly-supervised network that fuses 2D-3D deep features via MIP to further improve segmentation performance.
  arXiv  Detail & Related papers  (2024-02-19T13:24:46Z)
• Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations [1.7413461132662074]
  We propose a novel model, denoted a multi-dimension unified Swin transformer (MDU-ST) for 3D lesion segmentation. The network's performance is evaluated by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D lesion dataset. The proposed method can be used to conduct automated 3D lesion segmentation to assist radiomics and tumor growth modeling studies.
  arXiv  Detail & Related papers  (2023-09-04T21:24:00Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• 3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks [1.8244763402770727]
  3DAngioNet is a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach learns a coarse mesh template using an EfficientB3-UNet segmentation network and projection geometries, and deforms it using a graph convolutional network.
  arXiv  Detail & Related papers  (2023-02-28T17:46:25Z)
• Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling [3.435923468974656]
  Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been constructed with explicit representations such as meshes and voxel masks. Here, we propose to represent surfaces by the zero level set of their signed distance function in a differentiable implicit neural representation (INR) This allows us to model complex vascular structures with a representation that is implicit, continuous, light-weight, and easy to integrate with deep learning algorithms.
  arXiv  Detail & Related papers  (2022-07-29T13:08:35Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation [0.0]
  3D to 2D retinal vessel segmentation is a challenging problem in Optical Coherence Tomography Angiography ( OCTA) images. We propose a Progressive Attention-Enhanced Network (PAENet) based on attention mechanisms to extract rich feature representation. Our proposed algorithm achieves state-of-the-art performance compared with previous methods.
  arXiv  Detail & Related papers  (2021-08-26T10:27:25Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)

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.