Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI

• URL: http://arxiv.org/abs/2004.09788v3
• Date: Sun, 31 May 2020 01:47:33 GMT
• Title: Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI
• Authors: Jinyoung Kim, Remi Patriat, Jordan Kaplan, Oren Solomon, Noam Harel
• Abstract summary: Deep cerebellar nuclei are a key structure of the cerebellum that process motor and sensory information. It is challenging to clearly visualize such small nuclei under standard clinical magnetic resonance imaging (MRI) protocols. Recent advances in 7 Tesla (T) MRI technology and great potential of deep neural networks facilitate automatic patient-specific segmentation. We propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI.
• Score: 10.706353524285346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep cerebellar nuclei are a key structure of the cerebellum that are involved in processing motor and sensory information. It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment. However, it is challenging to clearly visualize such small nuclei under standard clinical magnetic resonance imaging (MRI) protocols and therefore precise segmentation is not feasible. Recent advances in 7 Tesla (T) MRI technology and great potential of deep neural networks facilitate automatic patient-specific segmentation. In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI. DCN-Net effectively encodes contextual information on the patch images without consecutive pooling operations and adding complexity via proposed dilated dense blocks. During the end-to-end training, label probabilities of dentate and interposed nuclei are independently learned with a hybrid loss, handling highly imbalanced data. Finally, we utilize self-training strategies to cope with the problem of limited labeled data. To this end, auxiliary dentate and interposed nuclei labels are created on unlabeled data by using DCN-Net trained on manual labels. We validate the proposed framework using 7T B0 MRIs from 60 subjects. Experimental results demonstrate that DCN-Net provides better segmentation than atlas-based deep cerebellar nuclei segmentation tools and other state-of-the-art deep neural networks in terms of accuracy and consistency. We further prove the effectiveness of the proposed components within DCN-Net in dentate and interposed nuclei segmentation.

Related papers

• Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation [0.6767885381740952]
  Training neural networks using limited annotations is an important problem in the medical domain. Semi-supervised learning aims to overcome this problem by learning segmentations with very little annotated data. We propose a framework based on superpixels to improve the accuracy of the pseudo labels.
  arXiv  Detail & Related papers  (2021-10-16T15:17:11Z)
• Complex-valued Federated Learning with Differential Privacy and MRI Applications [51.34714485616763]
  We introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of $f$-DP, $(varepsilon, delta)$-DP and R'enyi-DP. We present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task.
  arXiv  Detail & Related papers  (2021-10-07T14:03:00Z)
• Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images [65.47507533905188]
  We propose a novel multitask learning network with a bending loss regularizer to separate overlapped nuclei accurately. The newly proposed multitask learning architecture enhances the generalization by learning shared representation from three tasks. The proposed bending loss defines high penalties to concave contour points with large curvatures, and applies small penalties to convex contour points with small curvatures.
  arXiv  Detail & Related papers  (2021-09-30T17:29:44Z)
• DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation [0.0]
  We propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. The proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.
  arXiv  Detail & Related papers  (2021-05-17T15:43:59Z)
• 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)
• Automated Segmentation of Brain Gray Matter Nuclei on Quantitative Susceptibility Mapping Using Deep Convolutional Neural Network [16.733578721523898]
  Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases. We propose a double-branch residual-structured U-Net (DB-ResUNet) based on 3D convolutional neural network (CNN) to automatically segment such brain gray matter nuclei.
  arXiv  Detail & Related papers  (2020-08-03T14:32:30Z)
• Instance-aware Self-supervised Learning for Nuclei Segmentation [47.07869311690419]
  We propose a novel self-supervised learning framework to exploit the capacity of convolutional neural networks (CNNs) on the nuclei instance segmentation task. The proposed approach involves two sub-tasks, which enable neural networks to implicitly leverage the prior-knowledge of nuclei size and quantity. Experimental results on the publicly available MoNuSeg dataset show that the proposed self-supervised learning approach can remarkably boost the segmentation accuracy of nuclei instance.
  arXiv  Detail & Related papers  (2020-07-22T03:37:14Z)
• Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification [49.64863177155927]
  We propose a multi-task deep neural network with spatial activation mechanism to segment full retinal vessel, artery and vein simultaneously. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks.
  arXiv  Detail & Related papers  (2020-07-18T05:46:47Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Recurrent convolutional neural networks for mandible segmentation from computed tomography [0.9851812512860351]
  We propose a recurrent segmentation convolutional neural network (RSegCNN) that embeds segmentation convolutional neural network (SegCNN) into the recurrent neural network (RNN) Such a design of the system takes into account the similarity and continuity of the mandible shapes captured in adjacent image slices in CT scans. The RSegCNN is significantly better than the state-of-the-art models for accurate mandible segmentation.
  arXiv  Detail & Related papers  (2020-03-13T21:11:28Z)
• DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction [1.9199289015460215]
  We present a fully-convolutional, deep, and densely-connected encoder-decoder for pixel-wise semantic segmentation. The proposed network provides superior performance for semantic segmentation problems applied to open-source benchmarks.
  arXiv  Detail & Related papers  (2020-02-04T22:39:58Z)

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.