Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection

• URL: http://arxiv.org/abs/2107.09899v1
• Date: Wed, 21 Jul 2021 06:35:52 GMT
• Title: Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
• Authors: Runnan Chen, Yuexin Ma, Nenglun Chen, Lingjie Liu, Zhiming Cui, Yanhong Lin, Wenping Wang
• Abstract summary: We propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection. SA-LSTM first locates the coarse landmarks via heatmap regression on a down-sampled CBCT volume. It then progressively refines landmarks by attentive offset regression using high-resolution cropped patches. Experiments show that our method significantly outperforms state-of-the-art methods in terms of efficiency and accuracy.
• Score: 37.031819721889676
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, we propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection. To reduce the computational burden, SA-LSTM is designed in two stages. It first locates the coarse landmarks via heatmap regression on a down-sampled CBCT volume and then progressively refines landmarks by attentive offset regression using high-resolution cropped patches. To boost accuracy, SA-LSTM captures global-local dependence among the cropping patches via self-attention. Specifically, a graph attention module implicitly encodes the landmark's global structure to rationalize the predicted position. Furthermore, a novel attention-gated module recursively filters irrelevant local features and maintains high-confident local predictions for aggregating the final result. Experiments show that our method significantly outperforms state-of-the-art methods in terms of efficiency and accuracy on an in-house dataset and a public dataset, achieving 1.64 mm and 2.37 mm average errors, respectively, and using only 0.5 seconds for inferring the whole CBCT volume of resolution 768*768*576. Moreover, all predicted landmarks are within 8 mm error, which is vital for acceptable cephalometric analysis.

Related papers

• DETR Doesn't Need Multi-Scale or Locality Design [69.56292005230185]
  This paper presents an improved DETR detector that maintains a "plain" nature. It uses a single-scale feature map and global cross-attention calculations without specific locality constraints. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints.
  arXiv  Detail & Related papers  (2023-08-03T17:59:04Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)
• Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images [15.7026400415269]
  We propose a novel learning-to-learn framework for landmark detection. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.
  arXiv  Detail & Related papers  (2021-05-19T13:39:18Z)
• Delving into Localization Errors for Monocular 3D Object Detection [85.77319416168362]
  Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving. In this work, we quantify the impact introduced by each sub-task and find the localization error' is the vital factor in restricting monocular 3D detection.
  arXiv  Detail & Related papers  (2021-03-30T10:38:01Z)
• Automated 3D cephalometric landmark identification using computerized tomography [1.4349468613117398]
  Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level. This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed.
  arXiv  Detail & Related papers  (2020-12-16T07:29:32Z)
• 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)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound [16.523977092204813]
  3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks accurately. We exploit an object detection framework to detect landmarks in 3D fetal facial US volumes.
  arXiv  Detail & Related papers  (2020-04-01T03:00:03Z)
• Volumetric landmark detection with a multi-scale shift equivariant neural network [16.114319747246334]
  We propose a multi-scale, end-to-end deep learning method that achieves fast and memory-efficient landmark detection in 3D images. We evaluate our method for carotid artery bifurcations detection on 263 CT volumes and achieve a better than state-of-the-art accuracy with mean Euclidean distance error of 2.81mm.
  arXiv  Detail & Related papers  (2020-03-03T17:06:19Z)

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.