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A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

Mikael Agn, Ian Law, Per Munck af Rosenschöld, Koen Van Leemput, Martin A. Styner, Elsa D. Angelini
2016 Medical Imaging 2016: Image Processing  
The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape.  ...  We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images.  ...  The method combines an existing whole-brain segmentation method with a tumor prior that uses convolutional restricted Boltzmann machines to model tumor shape.  ... 
doi:10.1117/12.2216814 dblp:conf/miip/AgnLRL16 fatcat:6hzsj5mu7ja4dmy3zkfdupczkm

Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

Srinivasalu Preethi, Palaniappan Aishwarya
2017 Journal of Intelligent Systems  
Then, we categorize the brain image based on the selected features using the DNN.  ...  AbstractA brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body.  ...  When the RBM is trained, a dissimilar RBM can be "stacked" on top of it to form a multilayer model.  ... 
doi:10.1515/jisys-2017-0090 fatcat:uwomo3z35bgw5gzbouwfwnw7wa

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

Yingqian Liu, Zhuangzhi Yan
2020 Sensors  
The active contour model (ACM) with a statistical shape prior is robust.  ...  The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation.  ...  This restriction facilitates inference with the model. As a generative model, RBM has been used to model brain tumor [13] and lung shape [14] .  ... 
doi:10.3390/s20133628 pmid:32605230 pmcid:PMC7374374 fatcat:7fr3fnf53rdtdpfuwcws2mrbwe

State-of-the-art review on deep learning in medical imaging

Jasjit S Suri
2019 Frontiers in Bioscience  
Review on deep learning in medical imaging 393  ...  In segmentation, deformable models (33) are generally used for inferring the shape of infected/abnormal region in a medical image.  ...  5) By using the output of hidden layer in an RBM, as the input of visible layer to another RBM, a stack of RBMs can be created which effectively is the Deep Belief Network (DBN).  ... 
doi:10.2741/4725 fatcat:lh5b3okh4jcq5aogjfowjfdaqy

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Otman Basir, Kalifa Shantta
2021 IRA-International Journal of Applied Sciences (ISSN 2455-4499)  
Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.  ...  Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines.  ...  The tumor is primarily segmented from the MRI images applying an improved ICA composite model. After the segmented image, deep attributes are extracted and organized.  ... 
doi:10.21013/jas.v16.n1.p1 fatcat:yab26jaedrhrrlax5xm323xfje

Deep Learning Trends for Focal Brain Pathology Segmentation in MRI [chapter]

Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, Pierre-Marc Jodoin
2016 Lecture Notes in Computer Science  
In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation.  ...  Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease  ...  -There are no reliable shape or intensity priors for brain tumors/lesions.  ... 
doi:10.1007/978-3-319-50478-0_6 fatcat:vuheit2riffn3aun5u4rchgmgi

A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

2019 International journal of recent technology and engineering  
For early detection or prediction of the brain tumor, an improved feature extraction technique along with Deep Neural Network (DNN) has been recommended.  ...  Early tumor detection in brain plays vital role in early tumor detection and radiotherapy. MR images are used as the input image for brain tumor finding and classify the type of brain tumor.  ...  MRI gives rich data to brain tumor conclusion and treatment arranging. As a brain tumor changes in size, shape and intensity, makes tumor segmentation process progressively repetitive.  ... 
doi:10.35940/ijrte.b1193.0782s419 fatcat:wgzytnphlbeqlc5fyfswllkcqi

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded.  ... 
doi:10.1016/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Messaoud Hameurlaine, Abdelouahab Moussaoui
2019 Nano Biomedicine and Engineering  
Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing.  ...  There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.  ...  Generally, a machine learning classification method for brain tumor segmentation requires large amounts of brain MRI scans with known ground truth from different cases to train on.  ... 
doi:10.5101/nbe.v11i2.p178-191 fatcat:gh5jemeth5hapa62bomn7ypwgm

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
2017 Contrast Media & Molecular Imaging  
Research on cancer molecular images using deep learning techniques is also increasing dynamically.  ...  Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction.  ...  combined with an appropriate tumor segmentation technique.  ... 
doi:10.1155/2017/9512370 pmid:29114182 pmcid:PMC5661078 fatcat:ev3zrlx67vfo5mt23e5u3y2t64

From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities [article]

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
2019 arXiv   pre-print
The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest.  ...  models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future.  ...  Machines (RBMs) on top of each other where the RBM is an unsupervised two layer stochastic neural network that can model probabilistic dependencies with the objective of minimizing the reconstruction  ... 
arXiv:1808.07954v3 fatcat:huc23wcklfey5aetnlbe6o4h34

An Alarm System For Segmentation Algorithm Based On Shape Model [article]

Fengze Liu, Yingda Xia, Dong Yang, Alan Yuille, Daguang Xu
2019 arXiv   pre-print
During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values.  ...  Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data.The shape  ...  Unlike [26] which needs to pre-train with RBM, AE can be trained following an end-to-end fashion. [18] learned the shape representation from point cloud form, while we choose the volumetric form as a  ... 
arXiv:1903.10645v3 fatcat:ok4i4tap6rg4roewfrbteqve44

Trends in Deep Learning for Medical Hyperspectral Image Analysis

Uzair Khan, Sidike Paheding, Colin Elkin, Vijay Devabhaktuni
2021 IEEE Access  
One particular study used 27 a combination of 2D-3D CNN framework with a patch-based 28 approach for the input to aid brain tumor resection which 29 delivered an overall mean accuracy of 80% [58].  ...  A majority of the segmentation techniques involve u-88 net in one way or another, with it forming the core component of a 89 proposed model for segmentation.  ... 
doi:10.1109/access.2021.3068392 fatcat:mxse6n6f7bbbrognlnbzponr7u

Image segmentation algorithm based on grey model MRF

2008 Journal of Computer Applications  
During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values.  ...  The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks.  ...  Unlike [26] which needs to pre-train with RBM, AE can be trained following an end-to-end fashion. [18] learned the shape representation from point cloud form, while we choose the volumetric form as a  ... 
doi:10.3724/sp.j.1087.2008.00686 fatcat:xlvj53uqqbcbbiwixs4wyulety

Recapitulating in vivo-like plasticity of glioma cell invasion along blood vessels and in astrocyte-rich stroma

Pavlo Gritsenko, William Leenders, Peter Friedl
2017 Histochemistry and Cell Biology  
The invasion patterns in vitro were validated using histological analysis of brain sections from glioblastoma patients and glioma xenografts infiltrating the mouse brain.  ...  In conjunction, these organotypic assays enable a range of invasion modes used by glioma cells and will be applicable for mechanistic analysis and targeting of glioma cell dissemination.  ...  Introduction Gliomas represent the most common primary brain tumor type in adults, with glioblastoma as one of the most detrimental cancers in humans (Wen and Reardon 2016) .  ... 
doi:10.1007/s00418-017-1604-2 pmid:28825130 pmcid:PMC5602046 fatcat:njci6apw7fhvdaiabg63bxwyse
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