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9th International Symposium on Signal Processing and its Applications

作者： Zouagui, T. Chereul, E. Odet, C. Janier, M. Univ Sci Oran USTO LSI Oran Algeria Animage Platform Lyon France Insa Lyon Creatis Villeurbanne France

ISBN: (纸本)9781424407781

To understand the mechanisms of human development, embryologists study the development of mouse embryos. The aim of the research, as presented in this paper, is to identify cardiac malformations which are a major cause of mortality in childhood. With this end in view, we developed expert computer based algorithms known as segmentation. The segmentation framework is based on a true 3d deformable models rather than a slice-by-slice 2d processing. It includes both a pre-processing step and the automatic segmentation. At this point, we tested our segmentation pipeline on the left and right ventricles (LV, RV) of different mouse embryos. Results on a standard data set with 5 mM of Gadolinium and TE of 10 ins are presented and compared with the manually segmented embryonic heart model done at Animage. We present also the segmentation of an abnormal mouse ventricle and the segmentation of the heart muscle.

关键词： 3d image segmentation deformable models mesh refinement mouse MRI cardiac images

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Three-dimensional segmentation of the tumor mass in computed tomographic images of neuroblastoma

Three-dimensional segmentation of the tumor mass in computed...

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Medical Imaging 2004 Conference

作者： deglint, HJ Rangayyan, RM Boag, GS Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

ISBN: (纸本)0819452831

Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature;furthermore, viable tumor, necrosis, fibrosis, and normal tissue are often intermixed: Rather than attempt to separate these tissue types into distinct regions, we propose to explore methods to delineate the normal structures expected in abdominal CT images, remove them from further consideration, and examine the remaining parts of the images for the tumor mass. We explore the use of fuzzy connectivity for this purpose. Expert knowledge provided by the radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are also incorporated in the segmentation algorithm. segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to chemotherapy and in the planning of delayed surgery for resection of the tumor. The performance of the algorithm is evaluated using cases acquired from the Alberta Children's Hospital.

关键词： 3d image segmentation neuroblastoma computed tomography fuzzy connectivity

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Centerline Extraction with Principal Curve Tracing to Improve 3d Level Set Esophagus segmentation in CT images

Centerline Extraction with Principal Curve Tracing to Improv...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Sila Kurugol Erhan Bas deniz Erdogmus Jennifer G. dy Gregory C. Sharp dana H. Brooks Dept. of Electrical and Computer Engineering Northeastern University Dept. of Rad. Oncology Mass. General Hospital and Harvard Medical School

ISBN: (纸本)9781424441211

For radiotherapy planning, contouring of target volume and healthy structures at risk in CT volumes is essential. To automate this process, one of the available segmentation techniques can be used for many thoracic organs except the esophagus, which is very hard to segment due to low contrast In this work we propose to initialize our previously introduced model based 3d level set esophagus segmentation method with a principal curve tracing (PCT) algorithm, which we adapted to solve the esophagus centerline detection problem. To address challenges due to low intensity contrast, we enhanced the PCT algorithm by learning spatial and intensity priors from a small set of annotated CT volumes. To locate the esophageal wall, the model based 3d level set algorithm including a shape model that represents the variance of esophagus wall around the estimated centerline is utilized. Our results show improvement in esophagus segmentation when initialized by PCT compared to our previous work, where an ad hoc centerline initialization was performed. Unlike previous approaches, this work does not need a very large set of annotated training images and has similar performance.

关键词： Curve Tracing Level Sets CT 3d image segmentation Spatial Shape Model Radiation Oncology

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Voxel-Wise Cross-Volume Representation Learning for 3d Neuron Reconstruction 12th

Voxel-Wise Cross-Volume Representation Learning for 3D Neuro...

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12th International Workshop on Machine Learning in Medical Imaging (MLMI 2021)

作者： Wang, Heng Zhang, Chaoyi Yu, Jianhui Song, Yang Liu, Siqi Chrzanowski, Wojciech Cai, Weidong Univ Sydney Sch Comp Sci Sydney NSW Australia Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia Paige AI New York NY USA Univ Sydney Sydney Pharm Sch Sydney NSW Australia Univ Sydney Sydney Nano Inst Sydney NSW Australia

ISBN: (纸本)9783030875886;9783030875893

Automatic 3d neuron reconstruction is critical for analysing the morphology and functionality of neurons in brain circuit activities. However, the performance of existing tracing algorithms is hinged by the low image quality. Recently, a series of deep learning based segmentation methods have been proposed to improve the quality of raw 3d optical image stacks by removing noises and restoring neuronal structures from low-contrast background. due to the variety of neuron morphology and the lack of large neuron datasets, most of current neuron segmentation models rely on introducing complex and specially-designed submodules to a base architecture with the aim of encoding better feature representations. Though successful, extra burden would be put on computation during inference. Therefore, rather than modifying the base network, we shift our focus to the dataset itself. The encoder-decoder backbone used in most neuron segmentation models attends only intravolume voxel points to learn structural features of neurons but neglect the shared intrinsic semantic features of voxels belonging to the same category among different volumes, which is also important for expressive representation learning. Hence, to better utilise the scarce dataset, we propose to explicitly exploit such intrinsic features of voxels through a novel voxel-level cross-volume representation learning paradigm on the basis of an encoder-decoder segmentation model. Our method introduces no extra cost during inference. Evaluated on 42 3d neuron images from BigNeuron project, our proposed method is demonstrated to improve the learning ability of the original segmentation model and further enhancing the reconstruction performance.

关键词： deep learning Neuron reconstruction 3d image segmentation 3d optical microscopy

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Fully deformable 3d digital partition model with topological control

Fully deformable 3D digital partition model with topological...

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15th International Conference on discrete Geometry for Computer imagery

作者： damiand, Guillaume dupas, Alexandre Lachaud, Jacques-Olivier Univ Lyon CNRS LIRIS UMR5205 F-69622 Villeurbanne France Univ Poitiers CNRS SIC XLIM UMR6172 F-86962 Poitiers France Univ Savoie CNRS LAMA UMR5127 F-73376 Chambery France

We propose a purely discrete deformable partition model for segmenting 3d images. Its main ability is to maintain the topology of the partition during the minimization process. To do so, our main contribution is a new definition of multi-label simple points (ML simple point) that is easily computable. An ML simple point can be relabeled without modifying the overall topology of the partition. The definition is based on intervoxel properties, and uses the notion of collapse on cubical complexes. This work is an extension of a former restricted definition (dupas et al., 2009) that prohibits the move of intersections of boundary surfaces. A deformation process is carried out with a greedy energy minimization algorithm. A discrete area estimator is used to approach at best standard regularizers classically used in continuous energy minimizing methods. We illustrate the potential of our approach with the segmentation of 3d medical images with known expected topology. (C) 2010 Elsevier B.V. All rights reserved.

关键词： 3d image segmentation Simple point deformable model Intervoxel boundaries Multi-label image Cubical complexes

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Progressive Learning for Neuronal Population Reconstruction from Optical Microscopy images 22nd

Progressive Learning for Neuronal Population Reconstruction ...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhao, Jie Chen, Xuejin Xiong, Zhiwei Liu, dong Zeng, Junjie Zhang, Yueyi Zha, Zheng-Jun Bi, Guoqiang Wu, Feng Univ Sci & Technol China Natl Engn Lab Brain Inspired Intelligence Technol Hefei Peoples R China

ISBN: (纸本)9783030322397;9783030322380

Reconstruction of 3d neuronal populations from optical microscopy images is essential to investigate neural pathways and functions. This task is challenging because of the low signal-to-noise ratio and non-continuous intensities of neurite segments in optical microscopy images. Recently, significant improvement has been made on neuron reconstruction due to the development of deep neural networks (dNNs). Training such a dNN usually relies on a large number of images with voxel-wise annotations, and annotating these 3d images is very costly in terms of both finance and labor. In this paper, we propose a progressive learning strategy to take advantages of both traditional neuron tracing methods and deep learning techniques. Traditional neuron tracing techniques, which do not require expensive manual annotations for dense neurites, are employed to produce pseudo labels for neuron voxels. With the pseudo labels, a deep segmentation network is trained to learn discriminative and comprehensive features for neuron voxel extraction from noisy backgrounds. The neuron tracing module and the segmentation network can be mutually complemented and progressively improved to reconstruct more complete neuronal populations without using manual annotations. Moreover, we build a dataset called "VISoR-40" that consists of 40 optical microscopy 3d images from mouse cortical regions to demonstrate the superior performance of our progressive learning method.

关键词： deep learning Neuronal population reconstruction 3d image segmentation Optical microscopy

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On the improvement of the Topological Active Volumes model - A tetrahedral approach

On the improvement of the Topological Active Volumes model -...

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3rd International Conference on Computer Vision Theory and Applications

作者： Barreira, N. Penedo, M. G. Ortega, M. Rouco, J. Univ A Coruna Dept Comp Sci VARPA Grp La Coruna Spain

ISBN: (纸本)9789898111210

The Topological Active Volumes model is a 3d active model focused on segmentation and reconstruction tasks. The segmentation process is based on the adjustment of a 3d mesh composed of polyhedra. This adjustment is guided by the minimisation of several energy functions related to the mesh. Even though the original cubic mesh achieves good segmentation results, it hits difficulties in some cases due to its shape. This paper proposes a new topology for the TAV mesh based on tetrahedra that overcomes the cubic mesh difficulties. Also, the paper explains an improvement in the tetrahedral topology to increase the accuracy of the results as well as the efficiency of the overall process.

关键词： 3d image segmentation deformable models active models Topological Active Volumes

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In situ quantification of genomic instability in breast cancer progression

In situ quantification of genomic instability in breast canc...

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25th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： de Solórzano, CO Chin, K Gray, JW Lockett, SJ Lawrence Berkeley Lab Div Life Sci Berkeley CA USA

ISBN: (纸本)0780377893

Genomic instability is a hallmark of breast and other solid cancers. Presumably caused by critical telomere reduction, GI is responsible for providing the genetic diversity required in the multi-step progression of the disease. We have used multicolor fluorescence in situ hybridization and 3d image analysis to quantify genomic instability cell-by-cell in thick, intact tissue sections of normal breast epithelium preneoplastic lesions (usual ductal hyperplasia), ductal carcinona is situ or invasive carcinoma of the breast.. Our in situ -cell by cell- analysis of genomic instability shows an important increase of genomic instability in the transition from hyperplasia to in situ carcinoma, followed by a reduction of instability in invasive carcinoma. This pattern suggests that the transition from hyperplasia to in situ carcinoma corresponds to telomere crisis and invasive carcinoma is a consequence of telomerase reactivation afer telomere crisis.

关键词： 3d image segmentation genomic instability telomere crisis FISH

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INTERACTIVE 3-dIMENSIONAL segmentation METHOd BASEd ON REGION GROWING METHOd

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SYSTEMS ANd COMPUTERS IN JAPAN 1994年第1期25卷 88-97页

作者： SEKIGUCHI, H SANO, K YOKOYAMA, T HITACHI LTD CENT RES LABKOKUBUNJITOKYO 185JAPAN

It has become easy to obtain three-dimensional (3d) data of the inside of the human body because of developments in X-ray computed tomography (CT) and magnetic resonance imaging (MRI). The 3d images of internal organs constructed from these data are useful in understanding the 3d shapes and positional relationships of the organs and are expected to be the diagnostic images of the next generation. However, many problems remain to be solved in making them practical, such as, improving the display speed of 3d images and implementing a 3d interface. For MRI, in particular, the difficulty in segmenting the display image is becoming a major impediment to the practical use of 3d MRI images. In this paper, we propose a practical segmentation method in which interactive corrective operations for 3d images are included in 3d region growing. To implement this method, we developed a 3d monitoring function for the segmentation process and an automatic function for removing the leaks generated during region growing. This method was applied to several 3d MRI data of the head and its effectiveness was confirmed.

关键词： 3d image segmentation MAGNETIC RESONANCE image (MRI) REGION GROWING TECHNIQUE

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Constrained Spectral Clustering of Individual Trees in dense Forest Using Terrestrial Laser Scanning data

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REMOTE SENSING 2018年第7期10卷 1056-1056页

作者： Heinzel, Johannes Huber, Markus O. Swiss Fed Inst Forest Snow & Landscape Res WSL Zurcherstr 111 CH-8903 Birmensdorf Switzerland

The present study introduces an advanced method for 3d segmentation of terrestrial laser scanning data into single tree clusters. It intentionally tackled difficult forest situations with dense and structured tree formations, which inventory practitioners are often faced with. The strongly interlocking tree crowns of different sizes and in different layers characterized the test conditions of close to nature forest plots. Volumetric 3d images of the plots were derived from the original point cloud data. A clustering method with automatically derived priors focused on the segmentation of these images by global optimization. Therefore, each image was segmented as a whole and partitioned into individual tree objects using a combination of state-of-the-art techniques. Multiple steps were combined in a workflow that included a morphological detection of the tree stems, the construction of a similarity graph from the image data, the computation of the eigenspectrum which was weighted with the tree stem priors and the final labelling of the transformed data points in a Markov Random Field framework. The detected trees were verified by number and position which allowed for comparison with other studies. Additionally, for a subset of the data, we provided a detailed verification of the three-dimensional extent of the complete trees. The detection rate by number and position was 97.40% for major trees with a stem diameter at breast height (dBH) 12 cm and 84.62% for regeneration trees with a dBH < 12 cm. The three-dimensional extent of the detected trees resulted in an average producer's accuracy of 93.66% and a user's accuracy of 94.06%. Overall, these numbers confirm the capacity of the method for accurate segmentation of strongly layered and understory trees. Future studies could test the method on wider areas with large scale data and different forest types in order to determine its general transferability.

关键词： tree detection dense forest TLS forestry 3d image segmentation

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