BezierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images

• URL: http://arxiv.org/abs/2108.00760v1
• Date: Mon, 2 Aug 2021 10:10:57 GMT
• Title: BezierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images
• Authors: Haichou Chen, Yishu Deng, Bin Li, Zeqin Li, Haohua Chen, Bingzhong Jing and Chaofeng Li
• Abstract summary: We propose the BezierSeg model which outputs bezier curves encompassing the region of interest. directly modelling the contour with analytic equations ensures that the segmentation is connected, continuous, and the boundary is smooth. Experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.
• Score: 4.155973181136616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries such methods usually result in glitches, discontinuity, or disconnection, inconsistent with the fact that lesions are solid and smooth. To overcome these undesirable artifacts, we propose the BezierSeg model which outputs bezier curves encompassing the region of interest. Directly modelling the contour with analytic equations ensures that the segmentation is connected, continuous, and the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of accuracy, the bezier contour can be resampled and overlaid with images of any resolution. Moreover, a doctor can conveniently adjust the curve's control points to refine the result. Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.

Related papers

• Image-level Regression for Uncertainty-aware Retinal Image Segmentation [3.7141182051230914]
  We introduce a novel Uncertainty-Aware (SAUNA) transform, which adds pixel uncertainty to the ground truth. Our results indicate that the integration of the SAUNA transform and these segmentation losses led to significant performance boosts for different segmentation models.
  arXiv  Detail & Related papers  (2024-05-27T04:17:10Z)
• RecDiffusion: Rectangling for Image Stitching with Diffusion Models [53.824503710254206]
  We introduce a novel diffusion-based learning framework, textbfRecDiffusion, for image stitching rectangling. This framework combines Motion Diffusion Models (MDM) to generate motion fields, effectively transitioning from the stitched image's irregular borders to a geometrically corrected intermediary.
  arXiv  Detail & Related papers  (2024-03-28T06:22:45Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• Parallax-Tolerant Unsupervised Deep Image Stitching [57.76737888499145]
  We propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. To further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks.
  arXiv  Detail & Related papers  (2023-02-16T10:40:55Z)
• BoundarySqueeze: Image Segmentation as Boundary Squeezing [104.43159799559464]
  We propose a novel method for fine-grained high-quality image segmentation of both objects and scenes. Inspired by dilation and erosion from morphological image processing techniques, we treat the pixel level segmentation problems as squeezing object boundary. Our method yields large gains on COCO, Cityscapes, for both instance and semantic segmentation and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting.
  arXiv  Detail & Related papers  (2021-05-25T04:58:51Z)
• 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)
• Superpixel-Guided Label Softening for Medical Image Segmentation [31.989873877526424]
  We propose superpixel-based label softening for medical image segmentation. We show that this method achieves overall superior segmentation performances to baseline and comparison methods for both 3D and 2D medical images.
  arXiv  Detail & Related papers  (2020-07-17T10:55:59Z)
• SegFix: Model-Agnostic Boundary Refinement for Segmentation [75.58050758615316]
  We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by the empirical observation that the label predictions of interior pixels are more reliable, we propose to replace the originally unreliable predictions of boundary pixels by the predictions of interior pixels.
  arXiv  Detail & Related papers  (2020-07-08T17:08:08Z)
• A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations [10.761046991755311]
  We propose a spatially constrained deep convolutional neural network (DCNN) to achieve smooth and robust image segmentation. The proposed method has been evaluated based on corneal confocal microscopic ( CCM) images for nerve fiber segmentation.
  arXiv  Detail & Related papers  (2020-04-20T16:56:13Z)
• Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology [1.6506888719932784]
  We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship. We demonstrate the application of our method in medical microscopy image segmentation; particularly, in segmenting renal glomerular micro-environment in renal pathology.
  arXiv  Detail & Related papers  (2020-02-05T01:20:28Z)

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.