Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

• URL: http://arxiv.org/abs/2005.06368v1
• Date: Wed, 13 May 2020 15:23:44 GMT
• Title: Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
• Authors: Wietske A.P. Bastiaansen, Melek Rousian, R\'egine P.M. Steegers-Theunissen, Wiro J. Niessen, Anton Koning and Stefan Klein
• Abstract summary: We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound.
• Score: 3.8874909016794463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound.

Related papers

• A benchmark for 2D foetal brain ultrasound analysis [0.3742372933871118]
  We present a set of 104 2D foetal brain ultrasound images acquired during the 20th week of gestation. The images have been annotated to highlight landmark points from structures of interest to analyse brain development.
  arXiv  Detail & Related papers  (2024-06-25T03:34:54Z)
• FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound [28.408626329596668]
  We propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. First, we propose a scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. Third, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online.
  arXiv  Detail & Related papers  (2023-10-30T06:18:47Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Anatomy-guided domain adaptation for 3D in-bed human pose estimation [62.3463429269385]
  3D human pose estimation is a key component of clinical monitoring systems. We present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain. Our method consistently outperforms various state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2022-11-22T11:34:51Z)
• CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation [3.1657395760137406]
  We propose a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation. The experimental results demonstrate the superiority of the proposed framework over the baseline method both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-05-19T14:42:49Z)
• Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning [4.2698418800007865]
  3D ultrasound (US) can be used to image the uterus, but finding the position of uterine boundary in US images is a challenging task. We developed 2D UNet-based networks that are trained based on two scenarios.
  arXiv  Detail & Related papers  (2021-09-20T03:13:57Z)
• Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction [61.62191904755521]
  3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. Existing deep learning based methods only focus on the basic cases of skill sequences. We propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
  arXiv  Detail & Related papers  (2021-07-31T16:06:50Z)
• Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning [62.13520959168732]
  We propose a semi-supervised learning framework for instrument segmentation in 3D US. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. Our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume.
  arXiv  Detail & Related papers  (2021-07-30T07:59:45Z)
• Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation [0.0]
  We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by the proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16.
  arXiv  Detail & Related papers  (2020-11-23T17:11:50Z)
• Neural Descent for Visual 3D Human Pose and Shape [67.01050349629053]
  We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image. We rely on a recently introduced, expressivefull body statistical 3d human model, GHUM, trained end-to-end. Central to our methodology, is a learning to learn and optimize approach, referred to as HUmanNeural Descent (HUND), which avoids both second-order differentiation.
  arXiv  Detail & Related papers  (2020-08-16T13:38:41Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)

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.