Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)最新文献
Pub Date : 2016-10-17DOI: 10.1007/978-3-319-47118-1_2
Behrouz Saghafi, Geng Chen, F. Shi, P. Yap, D. Shen
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Accurate segmentation of infant hippocampus from Magnetic Resonance (MR) images is one of the key steps for the investigation of early brain development and neurological disorders. Since the manual delineation of anatomical structures is time-consuming and irreproducible, a number of automatic segmentation methods have been proposed, such as multi-atlas patch-based label fusion methods. However, the hippocampus during the first year of life undergoes dynamic appearance, tissue contrast and structural changes, which pose substantial challenges to the existing label fusion methods. In addition, most of the existing label fusion methods generally segment target images at each time-point independently, which is likely to result in inconsistent hippocampus segmentation results along different time-points. In this paper, we treat a longitudinal image sequence as a whole, and propose a spatial-temporal hypergraph based model to jointly segment infant hippocampi from all time-points. Specifically, in building the spatial-temporal hypergraph, (1) the atlas-to-target relationship and (2) the spatial/temporal neighborhood information within the target image sequence are encoded as two categories of hyperedges. Then, the infant hippocampus segmentation from the whole image sequence is formulated as a semi-supervised label propagation model using the proposed hypergraph. We evaluate our method in segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that, by leveraging spatial-temporal information, our method achieves better performance in both segmentation accuracy and consistency over the state-of-the-art multi-atlas label fusion methods.
{"title":"Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning.","authors":"Yanrong Guo, Pei Dong, Shijie Hao, Li Wang, Guorong Wu, Dinggang Shen","doi":"10.1007/978-3-319-47118-1_1","DOIUrl":"https://doi.org/10.1007/978-3-319-47118-1_1","url":null,"abstract":"<p><p>Accurate segmentation of infant hippocampus from Magnetic Resonance (MR) images is one of the key steps for the investigation of early brain development and neurological disorders. Since the manual delineation of anatomical structures is time-consuming and irreproducible, a number of automatic segmentation methods have been proposed, such as multi-atlas patch-based label fusion methods. However, the hippocampus during the first year of life undergoes dynamic appearance, tissue contrast and structural changes, which pose substantial challenges to the existing label fusion methods. In addition, most of the existing label fusion methods generally segment target images at each time-point independently, which is likely to result in inconsistent hippocampus segmentation results along different time-points. In this paper, we treat a longitudinal image sequence as a whole, and propose a spatial-temporal hypergraph based model to jointly segment infant hippocampi from all time-points. Specifically, in building the spatial-temporal hypergraph, (1) the atlas-to-target relationship and (2) the spatial/temporal neighborhood information within the target image sequence are encoded as two categories of hyperedges. Then, the infant hippocampus segmentation from the whole image sequence is formulated as a semi-supervised label propagation model using the proposed hypergraph. We evaluate our method in segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that, by leveraging spatial-temporal information, our method achieves better performance in both segmentation accuracy and consistency over the state-of-the-art multi-atlas label fusion methods.</p>","PeriodicalId":91784,"journal":{"name":"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-47118-1_1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36516208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First, we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second, besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third, since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.
脑干核团(红核和黑质)的精确分割在深部脑刺激和帕金森病(PD)成像生物标记物研究等各种神经成像应用中非常重要。由于衰老过程中的铁沉积,脑干在磁共振(MR)图像中的对比度非常低。因此,斑块相似性的模糊性使得最近成功的基于多图谱斑块的标签融合方法难以像从磁共振图像中分割皮层和皮层下区域那样具有竞争力。为了应对这一挑战,我们提出了一种使用深度超图学习的新型多图谱脑干核分割方法。具体来说,我们从三个方面实现了这一目标。首先,我们利用超图将基于图的分割方法在保持空间一致性方面的优势和基于多图谱框架的群体先验的优势结合起来。其次,除了使用低层次的图像外观,我们还提取高层次的上下文特征来衡量复杂的斑块关系。由于上下文特征是在初步估计的标签概率图上计算的,因此我们最终将基于超图学习的标签传播转化为深度自改进模型。第三,由于某些体素(通常位于统一区域)上的解剖学标签比其他体素(通常位于两个区域之间的边界)上的解剖学标签更可靠,因此我们允许这些可靠的体素将其标签传播到附近难以贴标的体素上。这种分层策略使我们提出的标签融合方法具有深度和动态性。我们在从 3.0 T MR 图像分割黑质(SN)和红核(RN)的过程中评估了我们提出的标签融合方法,与最先进的标签融合方法相比,我们提出的方法取得了显著的改进。
{"title":"Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.","authors":"Pei Dong, Yangrong Guo, Yue Gao, Peipeng Liang, Yonghong Shi, Qian Wang, Dinggang Shen, Guorong Wu","doi":"10.1007/978-3-319-47118-1_7","DOIUrl":"10.1007/978-3-319-47118-1_7","url":null,"abstract":"<p><p>Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. <i>First</i>, we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. <i>Second</i>, besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. <i>Third</i>, since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.</p>","PeriodicalId":91784,"journal":{"name":"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868975/pdf/nihms865291.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35957552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01Epub Date: 2016-09-22DOI: 10.1007/978-3-319-47118-1_8
Adrian V Dalca, Andreea Bobu, Natalia S Rost, Polina Golland
We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm.
{"title":"Patch-Based Discrete Registration of Clinical Brain Images.","authors":"Adrian V Dalca, Andreea Bobu, Natalia S Rost, Polina Golland","doi":"10.1007/978-3-319-47118-1_8","DOIUrl":"https://doi.org/10.1007/978-3-319-47118-1_8","url":null,"abstract":"<p><p>We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm.</p>","PeriodicalId":91784,"journal":{"name":"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-47118-1_8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35015484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01Epub Date: 2016-09-22DOI: 10.1007/978-3-319-47118-1_5
Lin Wang, Yanrong Guo, Xiaohuan Cao, Guorong Wu, Dinggang Shen
In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated group-mean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.
{"title":"Consistent Multi-Atlas Hippocampus Segmentation for Longitudinal MR Brain Images with Temporal Sparse Representation.","authors":"Lin Wang, Yanrong Guo, Xiaohuan Cao, Guorong Wu, Dinggang Shen","doi":"10.1007/978-3-319-47118-1_5","DOIUrl":"https://doi.org/10.1007/978-3-319-47118-1_5","url":null,"abstract":"<p><p>In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated group-mean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.</p>","PeriodicalId":91784,"journal":{"name":"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-47118-1_5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36553647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)