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IEEE Trans Med Imaging ; 41(9): 2360-2370, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-35377840


As connectomic datasets exceed hundreds of terabytes in size, accurate and efficient skeleton generation of the label volumes has evolved into a critical component of the computation pipeline used for analysis, evaluation, visualization, and error correction. We propose a novel topological thinning strategy that uses biological-constraints to produce accurate centerlines from segmented neuronal volumes while still maintaining biologically relevant properties. Current methods are either agnostic to the underlying biology, have non-linear running times as a function of the number of input voxels, or both. First, we eliminate from the input segmentation biologically-infeasible bubbles, pockets of voxels incorrectly labeled within a neuron, to improve segmentation accuracy, allow for more accurate centerlines, and increase processing speed. Next, a Convolutional Neural Network (CNN) detects cell bodies from the input segmentation, allowing us to anchor our skeletons to the somata. Lastly, a synapse-aware topological thinning approach produces expressive skeletons for each neuron with a nearly one-to-one correspondence between endpoints and synapses. We simultaneously estimate geometric properties of neurite width and geodesic distance between synapse and cell body, improving accuracy by 47.5% and 62.8% over baseline methods. We separate the skeletonization process into a series of computation steps, leveraging data-parallel strategies to increase throughput significantly. We demonstrate our results on over 1250 neurons and neuron fragments from three different species, processing over one million voxels per second per CPU with linear scalability.

Conectoma , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Esqueleto
Comput Biol Med ; 36(9): 974-96, 2006 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-16076463


A method for computationally efficient skeletonization of three-dimensional tubular structures is reported. The method is specifically targeting skeletonization of vascular and airway tree structures in medical images but it is general and applicable to many other skeletonization tasks. The developed approach builds on the following novel concepts and properties: fast curve-thinning algorithm to increase computational speed, endpoint re-checking to avoid generation of spurious side branches, depth-and-length sensitive pruning, and exact tree-branch partitioning allowing branch volume and surface measurements. The method was validated in computer and physical phantoms and in vivo CT scans of human lungs. The validation studies demonstrated sub-voxel accuracy of branch point positioning, insensitivity to changes of object orientation, and high reproducibility of derived quantitative indices of the tubular structures offering a significant improvement over previously reported methods (p<<0.001).

Brônquios/anatomia & histologia , Broncografia/métodos , Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Pulmão/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Simulação por Computador , Humanos , Modelos Anatômicos , Imagens de Fantasmas , Intensificação de Imagem Radiográfica/métodos , Reprodutibilidade dos Testes
IEEE Trans Med Imaging ; 24(12): 1540-7, 2005 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-16353371


Matching of corresponding branchpoints between two human airway trees, as well as assigning anatomical names to the segments and branchpoints of the human airway tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1% of the automatically assigned segment labels were found to be correct.

Brônquios/anatomia & histologia , Broncografia/métodos , Documentação/métodos , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Inteligência Artificial , Humanos , Intensificação de Imagem Radiográfica/métodos , Radiografia Torácica/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Técnica de Subtração
Acad Radiol ; 10(10): 1104-18, 2003 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-14587629


RATIONALE AND OBJECTIVES: Efforts to establish a quantitative approach to the computed tomography (CT)-based character ization of the lung parenchyma in interstitial lung disease (including emphysema) has been sought. The accuracy of these tools must be site independent. Multi-detector row CT has remained the gold standard for imaging the lung, and it provides the ability to image both lung structure as well as lung function. MATERIAL AND METHODS: Imaging is via multi-detector row CT and protocols include careful control of lung volume during scanning. Characterization includes not only anatomic-based measures but also functional measures including regional parameters derived from measures of pulmonary blood flow and ventilation. Image processing includes the automated detection of the lungs, lobes, and airways. The airways provide the road map to the lung parenchyma. Software automatically detects the airways, the airway centerlines, and the branch points, and then automatically labels the airway tree segments with a standardized set of labels, allowing for intersubject as well intrasubject comparisons across time. By warping all lungs to a common atlas, the atlas provides the range of normality for the various parameters provided by CT imaging. RESULTS: Imaged density and textural changes mark underlying structural changes at the most peripheral regions of the lung. Additionally, texture-based alterations in the parameters of blood flow may provide early evidence of pathologic processes. Imaging of stable xenon gas provides a regional measure of ventilation which, when coupled with measures of flow, provide for a textural analysis regional of ventilation-perfusion matching. CONCLUSION: With the improved resolution and speed of CT imaging, the patchy nature of regional parenchymal pathology can be imaged as texture of structure and function. With careful control of imaging protocols and the use of objective image analysis methods it is possible to provide site-independent tools for the assessment of interstitial lung disease. There remains a need to validate these methods, which requires interdisciplinary and cross-institutional efforts to gather appropriate data bases of images along with a consensus on appropriate ground truths associated with the images. Furthermore, there is the growing need for scanner manufacturers to focus on not just visually pleasing images, but on quantitatifiably accurate images.

Processamento de Imagem Assistida por Computador , Doenças Pulmonares Intersticiais/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Humanos , Pulmão/patologia , Pulmão/fisiologia , Doenças Pulmonares Intersticiais/patologia , Doenças Pulmonares Intersticiais/fisiopatologia , Ventilação Pulmonar
Inf Process Med Imaging ; 18: 222-33, 2003 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-15344460


A method for quantitative assessment of tree structures is reported allowing evaluation of airway or vascular tree morphology and its associated function. Our skeletonization and branch-point identification method provides a basis for tree quantification or tree matching, tree-branch diameter measurement in any orientation, and labeling individual branch segments. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. The proposed method has been tested in 343 computer phantom instances subjected to changes of its orientation as well as in a repeatedly CT-scanned rubber plastic phantom width sub-voxel accuracy and high reproducibility. Application to 35 human in vivo trees yielded reliable and well-positioned centerlines and branch-points.

Algoritmos , Broncografia/métodos , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Animais , Simulação por Computador , Humanos , Imageamento Tridimensional/instrumentação , Pulmão/diagnóstico por imagem , Imagens de Fantasmas , Intensificação de Imagem Radiográfica/instrumentação , Intensificação de Imagem Radiográfica/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/instrumentação , Radiografia Torácica/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Técnica de Subtração
IEEE Trans Med Imaging ; 21(3): 263-73, 2002 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-11989850


PURPOSE: Demonstration of a technique for three-dimensional (3-D) assessment of tracheal-stenoses, regarding site, length and degree, based on spiral computed tomography (S-CT). PATIENTS AND METHODS: S-CT scanning and automated segmentation of the laryngo-tracheal tract (LTT) was followed by the extraction of the LTT medial axis using a skeletonization algorithm. Orthogonal to the medial axis the LTT 3-D cross-sectional profile was computed and presented as line charts, where degree and length was obtained. Values for both parameters were compared between 36 patients and 18 normal controls separately. Accuracy and precision was derived from 17 phantom studies. RESULTS: Average degree and length of tracheal stenoses was found to be 60.5% and 4.32 cm in patients compared with minor caliber changes of 8.8% and 2.31 cm in normal controls (p << 0.0001). For the phantoms an excellent correlation between the true and computed 3-D cross-sectional profile was found (p << 0.005) and an accuracy for length and degree measurements of 2.14 mm and 2.53% respectively could be determined. The corresponding figures for the precision were found to be 0.92 mm and 2.56%. CONCLUSION: LTT 3-D cross-sectional profiles permit objective, accurate and precise assessment of LTT caliber changes. Minor LTT caliber changes can be observed even in normals and, in case of an otherwise normal S-CT study, can be regarded as artifacts.

Algoritmos , Imageamento Tridimensional/métodos , Tomografia Computadorizada Espiral/instrumentação , Tomografia Computadorizada Espiral/métodos , Estenose Traqueal/diagnóstico por imagem , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Endoscopia , Lógica Fuzzy , Humanos , Lactente , Pessoa de Meia-Idade , Imagens de Fantasmas , Reprodutibilidade dos Testes , Sensibilidade e Especificidade