Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound

• URL: http://arxiv.org/abs/2408.04826v1
• Date: Fri, 9 Aug 2024 02:55:25 GMT
• Title: Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound
• Authors: Yiming Chen, Niharika S. D'Souza, Akshith Mandepally, Patrick Henninger, Satyananda Kashyap, Neerav Karani, Neel Dey, Marcos Zachary, Raed Rizq, Paul Chouinard, Polina Golland, Tanveer F. Syeda-Mahmood,
• Abstract summary: Current segmentation networks like the UNet lack the precision needed for clinical adoption in IVUS. We propose the Geo-UNet framework to address these issues via a design informed by the geometry of the segmentation task. The efficacy of our framework on a venous IVUS dataset is shown against state-of-the-art models.
• Score: 7.760705377465734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segmentation networks like the UNet lack the precision needed for clinical adoption in IVUS workflows. This arises due to the difficulty of automatically learning accurate lumen contour from limited training data while accounting for the radial geometry of IVUS imaging. We propose the Geo-UNet framework to address these issues via a design informed by the geometry of the lumen contour segmentation task. We first convert the input data and segmentation targets from Cartesian to polar coordinates. Starting from a convUNet feature extractor, we propose a two-task setup, one for conventional pixel-wise labeling and the other for single boundary lumen-contour localization. We directly combine the two predictions by passing the predicted lumen contour through a new activation (named CDFeLU) to filter out spurious pixel-wise predictions. Our unified loss function carefully balances area-based, distance-based, and contour-based penalties to provide near clinical-grade generalization in unseen patient data. We also introduce a lightweight, inference-time technique to enhance segmentation smoothness. The efficacy of our framework on a venous IVUS dataset is shown against state-of-the-art models.

Related papers

• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• 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)
• HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling [3.485615723221064]
  We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine.
  arXiv  Detail & Related papers  (2023-11-21T09:58:39Z)
• Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph [49.11220791279602]
  It is challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications. A graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup.
  arXiv  Detail & Related papers  (2023-07-07T18:57:21Z)
• Robust Landmark-based Stent Tracking in X-ray Fluoroscopy [10.917460255497227]
  We propose an end-to-end deep learning framework for single stent tracking. It consists of three hierarchical modules: U-Net based landmark detection, ResNet based stent proposal and feature extraction. Experiments show that our method performs significantly better in detection compared with the state-of-the-art point-based tracking models.
  arXiv  Detail & Related papers  (2022-07-20T14:20:03Z)
• SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors [4.2663199451998475]
  We introduce a semi-supervised paradigm into the retinal layer segmentation task. In particular, a novel fully differentiable approach is used for converting surface position regression into a pixel-wise structured segmentation. In parallel, we propose a set of anatomical priors to improve network training when a limited amount of labeled data is available.
  arXiv  Detail & Related papers  (2022-07-01T14:30:59Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound [74.22397862400177]
  We propose a novel Frustum ultrasound based catheter segmentation method. The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
  arXiv  Detail & Related papers  (2020-10-19T13:56:22Z)
• Assignment Flow for Order-Constrained OCT Segmentation [0.0]
  The identification of retinal layer thicknesses serves as an essential task be done for each patient separately. The elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven textitgeometric approach to order-constrained 3D OCT retinal cell layer segmentation
  arXiv  Detail & Related papers  (2020-09-10T01:57:53Z)
• Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet [74.22397862400177]
  We propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data.
  arXiv  Detail & Related papers  (2020-06-25T21:10:04Z)

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.