Coupling Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and Measuring Spatial Structures in Images of Tumor Microenvironment

Zhu, Yanqiao; Li, Fuhai; Cridebring, Derek; Ma, Jinwen; Wong, Stephen T.C.; Vadakkan, Tegy J.; Zhang, Mei; Landua, John D.; Wei, Wei; Dickinson, Mary E.; Rosen, Jeffrey M.; Lewis, Michael T.

doi:10.1109/bibm.2011.104

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…In their work both shape and textural features are extracted from each supervoxel and are used to train an SVM classifier, which is then used in a CRF framework. The work of Zhu et al [25], [26] has applied supervoxels to the segmentation of 3-dimensional vasculature. In this work they consider a semi-supervised graph transduction approach using Gaussian affinities.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images

Bates

Irving

Markelc

et al. 2019

IEEE Trans. Med. Imaging

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Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods, however, particularly in the study of tumors it is of particular interest to be able to visualize both perfused and non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopy images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov Random Field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method out-performs both a state-ofthe- art method, specific to these data, as well as more classical vasculature segmentation methods.

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Section: Related Workmentioning

confidence: 99%

Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images

Bates

Irving

Markelc

et al. 2019

IEEE Trans. Med. Imaging

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show abstract

“…Owing to the intensity variation between vascular and avascular regions, local regions on vasculature often exhibit irregular shapes, whereas the local regions on avascular background have regular cube-like shapes, as shown in figure 2. Though rayburst-based shape descriptors are effective, the calculation of these features requires high computational cost [23,24]. Here, we employ a simpler descriptor to measure the shape irregularity of local regions.…”

Section: Shape Irregularity Featurementioning

confidence: 99%

“…Though rayburst-based shape descriptors are effective, the calculation of these features requires high computational cost [23,24]. Here, we employ a simpler descriptor to measure the shape irregularity of local regions.…”

Section: Shape Irregularity Featurementioning

confidence: 99%

Three-dimensional vasculature reconstruction of tumour microenvironment via local clustering and classification

Zhu

Vadakkan

et al. 2013

Interface Focus.

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The vasculature inside breast cancers is one important component of the tumour microenvironment. The investigation of its spatial morphology, distribution and interactions with cancer cells, including cancer stem cells, is essential for elucidating mechanisms of tumour development and treatment response. Using confocal microscopy and fluorescent markers, we have acquired three-dimensional images of vasculature within mammary tumours and normal mammary gland of mouse models. However, it is difficult to segment and reconstruct complex vasculature accurately from the in vivo three-dimensional images owing to the existence of uneven intensity and regions with low signal-to-noise ratios (SNR). To overcome these challenges, we have developed a novel three-dimensional vasculature segmentation method based on local clustering and classification. First, images of vasculature are clustered into local regions, whose boundaries well delineate vasculature even in low SNR and uneven intensity regions. Then local regions belonging to vasculature are identified by applying a semi-supervised classification method based on three informative features of the local regions. Comparison of results using simulated and real vasculature images, from mouse mammary tumours and normal mammary gland, shows that the new method outperforms existing methods, and can be used for three-dimensional images with uneven background and low SNR to achieve accurate vasculature reconstruction.

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Erythrocytes Morphological Classification Through HMM for Sickle Cell Detection

Delgado-Font¹,

González-Hidalgo²,

Herold-García³

et al. 2016

Articulated Motion and Deformable Objects

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Coupling Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and Measuring Spatial Structures in Images of Tumor Microenvironment

Cited by 5 publications

References 17 publications

Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images

Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images

Three-dimensional vasculature reconstruction of tumour microenvironment via local clustering and classification

Erythrocytes Morphological Classification Through HMM for Sickle Cell Detection

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