Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation

Xu, Chaoqing; Sun, Guodao; Liang, Ronghua; Xu, Xiufang

doi:10.1109/tcds.2021.3094555

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…In these studies, fibers from the whole brain are classified into anatomically meaningful fiber tracts based on labeled training datasets. To alleviate the requirement of ground truth labels, recent studies have shown the potential of using unsupervised deep learning for WMFC [41], [63]. In [63], an auto-encoder based neural network is adopted to achieve fiber clustering; however, it requires complex feature extraction procedures to generate inputs of the neural network.…”

Section: B Unsupervised Feature Learning and Clusteringmentioning

confidence: 99%

DFC: Anatomically Informed Fiber Clustering with Self-supervised Deep Learning for Fast and Effective Tractography Parcellation

Chen¹,

Zhang²,

Xue³

et al. 2022

Preprint

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White matter fiber clustering (WMFC) parcellates tractography data into anatomically meaningful fiber bundles, usually in an unsupervised manner without the need of labeled ground truth data. While widely used WMFC approaches have shown good performance using classical machine learning techniques, recent advances in deep learning reveal a promising direction towards fast and effective WMFC. In this work, we propose a novel deep learning framework for WMFC, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This accelerates the fiber representation learning to handle a known challenge in WMFC, i.e., the sensitivity of clustering results to the point ordering along fibers. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus DFC makes use of the combined white matter fiber geometry and gray matter anatomical parcellation to improve anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal in a natural way by rejecting fibers with low cluster assignment probabilities. We evaluate DFC on three independently acquired cohorts (including data from 220 subjects) and compare it to several state-ofthe-art WMFC algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency. In addition, DFC parcellates whole brain tractography with 50k fibers in about 1.5 minutes, providing a fast and efficient tool for large data analysis.

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Section: B Unsupervised Feature Learning and Clusteringmentioning

confidence: 99%

DFC: Anatomically Informed Fiber Clustering with Self-supervised Deep Learning for Fast and Effective Tractography Parcellation

Chen¹,

Zhang²,

Xue³

et al. 2022

Preprint

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show abstract

“…Over the past few decades, various medical image segmentation algorithm have been presented, which can be broadly grouped into thresholding ( Jain and Singh, 2022 ; Rawas and El-Zaart, 2022 ), watershed ( Mohanapriya and Kalaavathi, 2019 ; Sadegh et al, 2022 ), clustering ( Xu et al, 2022 ; Zhou et al, 2022 ), conditional random field ( Sun et al, 2020 ; Li et al, 2022 ), dictionary learning ( Yang Y Y et al, 2020 ; Tang et al, 2021 ), graph cut ( Gamechi et al, 2021 ; Zhu et al, 2021 ), region growing ( Rundo et al, 2016 ; Biratu et al, 2021 ), active contour ( Dake et al, 2019 ; Shahvaran et al, 2021 ), quantum-inspired computing ( Sergioli et al, 2021 ; Amin et al, 2022 ), computational intelligence ( Vijay et al, 2016 ; Zhang et al, 2022 ). These traditional methods rely on developers to design algorithms for specific applications.…”

Section: Introductionmentioning

confidence: 99%

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Zhang,

Cheng,

Zhou

et al. 2023

Front. Physiol.

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Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People’s Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

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“…Three clustering methods, namely K-means, hierarchical clustering, and spectral clustering, were used to effectively classify the chaotic streamline, which provided a favorable reference for further visualization of the streamline (Qi, 2015). The dynamic time regularization algorithm and the mean minimum distance between adjacent points were used to measure the similarity of streamlines, simplify the flow field, and improve the visualization (Lintao); and through streamline normalization and regularpolyhedron projection, high-dimensional features of each fiber tract are computed and fed to the IDEC clustering algorithm for clustering, which also provide qualitative and quantitative evaluations of the IDEC clustering method and QB clustering method (Xu et al, 2021). A streamline numerical simulation method (Hu and Lihui, 2018) was presented, which calculates the average attribute of streamlines and then applies a clustering method to identify the flow field of the water-drive reservoir.…”

Section: Introductionmentioning

confidence: 99%

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

et al. 2022

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The flow field obtained from streamline simulation reflects key properties of the reservoir, such as the distribution of the remaining oil and the location of channels. However, in the three-dimensional streamline field, the advantages of streamline simulation are limited. Because numerous streamlines interfere with each other and distribute in a sophisticated way, it is really difficult to infer the connectivity between wells and the flow characteristics of the reservoir. To make a more effective and visualizable description of the flow field, the three-dimensional streamline field has to be simplified. In this paper, principal component analysis (PCA) is applied to parameterize the streamline attributes and reduce the dimensionality of the flow field. After dimension reduction, the principal components of the streamline field can be analyzed by the clustering method. In the clustering procedure, the mainstream lines are selected according to the clustering center, thereby intuitively illustrating the properties of the reservoir. Through experimental verification, the proposed method can characterize the streamlines of the flow field more efficiently and reflect the inter-well connectivity more clearly than the commercial numerical simulator.

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Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation

Cited by 7 publications

References 59 publications

DFC: Anatomically Informed Fiber Clustering with Self-supervised Deep Learning for Fast and Effective Tractography Parcellation

DFC: Anatomically Informed Fiber Clustering with Self-supervised Deep Learning for Fast and Effective Tractography Parcellation

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

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