Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors

• URL: http://arxiv.org/abs/2007.01217v1
• Date: Thu, 2 Jul 2020 15:56:46 GMT
• Title: Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
• Authors: Leixin Zhou, Xiaodong Wu
• Abstract summary: We propose a novel model based on convolutional neural network (CNN) followed by a learnable surface smoothing block to tackle the surface segmentation problem. Experiments carried out on Spectral Domain Optical Coherence Tomography (SD- OCT) retinal layer segmentation and Intravascular Ultrasound (IVUS) vessel wall segmentation demonstrated very promising results.
• Score: 0.21756081703275998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated surface segmentation is important and challenging in many medical image analysis applications. Recent deep learning based methods have been developed for various object segmentation tasks. Most of them are a classification based approach, e.g. U-net, which predicts the probability of being target object or background for each voxel. One problem of those methods is lacking of topology guarantee for segmented objects, and usually post processing is needed to infer the boundary surface of the object. In this paper, a novel model based on convolutional neural network (CNN) followed by a learnable surface smoothing block is proposed to tackle the surface segmentation problem with end-to-end training. To the best of our knowledge, this is the first study to learn smoothness priors end-to-end with CNN for direct surface segmentation with global optimality. Experiments carried out on Spectral Domain Optical Coherence Tomography (SD-OCT) retinal layer segmentation and Intravascular Ultrasound (IVUS) vessel wall segmentation demonstrated very promising results.

Related papers

• LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion [79.22197702626542]
  This paper introduces a framework that explores amodal segmentation for robotic grasping in cluttered scenes. We propose a Linear-fusion Attention-guided Convolutional Network (LAC-Net) The results on different datasets show that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-08-06T14:50:48Z)
• NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function [55.86697795177619]
  Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. We introduce a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds. Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks.
  arXiv  Detail & Related papers  (2023-11-01T09:25:29Z)
• A deep learning network with differentiable dynamic programming for retina OCT surface segmentation [9.31543407418766]
  This study proposes to unify a U-Net for feature learning with a constrained differentiable dynamic programming module to achieve an end-to-end learning for retina OCT surface segmentation. It effectively utilizes the feedback from the downstream model optimization module to guide feature learning, yielding a better enforcement of global structures of the target surfaces.
  arXiv  Detail & Related papers  (2022-10-08T16:26:09Z)
• 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)
• End-to-end Neuron Instance Segmentation based on Weakly Supervised Efficient UNet and Morphological Post-processing [0.0]
  We present an end-to-end weakly-supervised framework to automatically detect and segment NeuN stained neuronal cells on histological images. We integrate the state-of-the-art network, EfficientNet, into our U-Net-like architecture.
  arXiv  Detail & Related papers  (2022-02-17T14:35:45Z)
• TSG: Target-Selective Gradient Backprop for Probing CNN Visual Saliency [72.9106103283475]
  We study the visual saliency, a.k.a. visual explanation, to interpret convolutional neural networks. Inspired by those observations, we propose a novel visual saliency framework, termed Target-Selective Gradient (TSG) backprop. The proposed TSG consists of two components, namely, TSG-Conv and TSG-FC, which rectify the gradients for convolutional layers and fully-connected layers, respectively.
  arXiv  Detail & Related papers  (2021-10-11T12:00:20Z)
• 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)
• Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning [6.11411524694755]
  Deep learning is emerging as powerful tools for medical image segmentation thanks to its superior feature learning capability. In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters. The multiple optimal surfaces are then simultaneously detected by minimizing the total surface cost while explicitly enforcing the mutual surface interaction constraints.
  arXiv  Detail & Related papers  (2020-07-02T17:13:35Z)
• Boundary-aware Context Neural Network for Medical Image Segmentation [15.585851505721433]
  Medical image segmentation can provide reliable basis for further clinical analysis and disease diagnosis. Most existing CNNs-based methods produce unsatisfactory segmentation mask without accurate object boundaries. In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation.
  arXiv  Detail & Related papers  (2020-05-03T02:35:49Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)
• Learning Fast and Robust Target Models for Video Object Segmentation [83.3382606349118]
  Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. Most previous approaches fine-tune segmentation networks on the first frame, resulting in impractical frame-rates and risk of overfitting. We propose a novel VOS architecture consisting of two network components.
  arXiv  Detail & Related papers  (2020-02-27T21:58:06Z)

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.