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Artificial intelligence in tumor subregion analysis based on medical imaging: A review

Mingquan Lin, Jacob F. Wynne, Boran Zhou, Tonghe Wang, Yang Lei, Walter J. Curran, Tian Liu, Xiaofeng Yang
2021 Journal of Applied Clinical Medical Physics  
Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised.  ...  Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis.  ...  Based on 18 F-FDG PET and contrast CT imaging, the primary tumor and involved lymph nodes were divided into subregions by individual-and population-level clustering.  ... 
doi:10.1002/acm2.13321 pmid:34164913 fatcat:5sdkrp6xnng43okx7gkvz6deau

A novel self-learning framework for bladder cancer grading using histopathological images

Gabriel García, Anna Esteve, Adrián Colomer, David Ramos, Valery Naranjo
2021 Computers in Biology and Medicine  
Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC).  ...  Specifically, we propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows for the classification of histological patches into different levels of disease severity, according  ...  tissue to determine the type of tumour growth.  ... 
doi:10.1016/j.compbiomed.2021.104932 pmid:34673472 fatcat:o4kf43ykdzeaddoej3qbt2h6za

On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

Carmelo Militello, Andrea Ranieri, Leonardo Rundo, Ildebrando D'Angelo, Franco Marinozzi, Tommaso Vincenzo Bartolotta, Fabiano Bini, Giorgio Russo
2021 Applied Sciences  
In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region  ...  These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation.  ...  Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering. Comput. Biol. Med. 2020, 120, 103751. [CrossRef] [PubMed] 42.  ... 
doi:10.3390/app12010162 fatcat:u5cycszgvnbi7ithuobcr2dtuu

Comprehensive computer-aided diagnosis for breast T1-weighted DCE-MRI through quantitative dynamical features and spatio-temporal local binary patterns

Gabriele Piantadosi, Stefano Marrone, Roberta Fusco, Mario Sansone, Carlo Sansone
2018 IET Computer Vision  
The research has been performed by means of the following databases:  ...  Since magnetic resonance imaging data includes different tissues and patient movements (i.e. breathing) may introduce artefacts during acquisition, CADs need some stages aimed to identify breast parenchyma  ...  The dataset also contains the following ground truth: the segmentation of breast tissues and a voxel-by-voxel segmentation of all the suspicious nodules (both benignant lesions and malignant tumours).  ... 
doi:10.1049/iet-cvi.2018.5273 fatcat:h2gvogcfczfabdl5z7d2weqdiu

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth Gerstner (+56 others)
2015 IEEE Transactions on Medical Imaging  
At the first stage, the goal is to coarsely segment tumours (and associated sub-classes) from surrounding healthy tissues using texture features.  ...  Tumour detection could either be skipped at the expense of higher computational burden, or be more sensitive by using the T 2 -weighted image in addition to the FLAIR image. • for enhancing tumors, thin  ... 
doi:10.1109/tmi.2014.2377694 pmid:25494501 pmcid:PMC4833122 fatcat:csrnfqc4i5eilh7wk5howvpr4u

A review of machine learning approaches, challenges and prospects for computational tumor pathology [article]

Liangrui Pan, Zhichao Feng, Shaoliang Peng
2022 arXiv   pre-print
better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics.  ...  However, tumor computational pathology now brings some challenges to the application of tumour screening, diagnosis and prognosis in terms of data integration, hardware processing, network sharing bandwidth  ...  An unsupervised tissue cluster-level map cut (TisCut) method divides histological images into meaningful intervals (e.g. tumour or non-tumour) to assist downstream supervised models of annotated histological  ... 
arXiv:2206.01728v1 fatcat:g7r7fsw2bzafpkkyg6hpzjyt5e

Medical image segmentation using deep learning: A survey

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi
2022 IET Image Processing  
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field.  ...  Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify  ...  [57] proposed a kind of cascade networks for whole-brain MRI and high-resolution mammogram segmentation.  ... 
doi:10.1049/ipr2.12419 fatcat:zvgj3vdzqbfbzjoglgmtnn6ukq

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data.  ...  In the other approach, each stage of the image formation is modeled as a linear system and the whole acquisition process is described by the cascaded sub-systems.  ...  2 cm) in tens of minutes yielding images containing millions of spectra. Spectra are then automatically classified as one of seven cell-types in prostate tissue in a matter of seconds.  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

Survival from cancer of the pancreas in England and Wales up to 2001

N Starling, D Cunningham
2008 British Journal of Cancer  
| 2 Morphogenetic features of pancreatic neoplasms 062 | 2.1 Classification of pancreatic neoplasms and their genetics (G. Kloeppel, J. Luettges, G. Zamboni and A.  ...  Merlos) 118 | 3.3 Genetic and cellular characteristics of pancreatic carcinoma cell lines (B. Sipos, FX Real, P. Moore, H. Kalthoff, A. Scarpa and G.  ...  QLG-CT-2002-01196).  ... 
doi:10.1038/sj.bjc.6604577 pmid:18813250 pmcid:PMC2557510 fatcat:6n26q7r2qbatnnsgawhc5nxvt4

Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor (+14 others)
2022 Information Fusion  
by different scanners and protocols to improve stability and robustness.  ...  based on different theories.  ...  supported by Boehringer Ingelheim Ltd, the European Union's Horizon 2020 research and innovation programme (ICOVID,  ... 
doi:10.1016/j.inffus.2022.01.001 pmid:35664012 pmcid:PMC8878813 fatcat:57zns35robfzxg5qojnyvntcyy

Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions [article]

Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor (+14 others)
2022 arXiv   pre-print
by different scanners and protocols to improve stability and robustness.  ...  based on different theories.  ...  [13] found a considerable segmentation based inconsistency of lung tumours while conducting repeated manual labelling by three radiologists.  ... 
arXiv:2201.06505v1 fatcat:fdb7l52kmbecvbz3styi52ttkm

ijair-volume-6-issue-1-vii-january-march-2019 -HINDUSTAN BOOK.pdf

V. Thamilarasi
2022 figshare.com  
This paper experiments various basic image segmentation techniques for Lung Chest X-Ray images  ...  cellular, tissue, organ, and whole-body systems.  ...  region growing segmentation method to segment CT scan lung images.  ... 
doi:10.6084/m9.figshare.20217722.v1 fatcat:l74ihuqhcvdtjomod3zdwzfniu

Abstracts (Continue in Part I)

1995 Proceedings of the International Society for Magnetic Resonance in Medicine  
It is therefore possible under these conditions and knowing BI to calculate T I , T2, and p images in the presence of inhomogeneos BI fields.  ...  This process is expected to improve multispectral tissue classification in situations where imaging coils are used which have large variations in BI fields.  ...  Acknowledeerneri ts The Institute of Neurology NMR Research Unit is funded by the Multiple Sclerosis Society of Great Britain and Northern Ireland.  ... 
doi:10.1002/mrmp.22419950202 fatcat:mg5plhp4qvabdbvpf3jlnih2ti

A DECISION MAKING SUPPORT SYSTEM IN NUCLEAR MEDICINE USING FUZZY COGNITIVE MAPS

Ioannis Dimitris Apostolopoulos, Peter P. Groumpos
2020 Zenodo  
Acknowledgement: This work was supported by the project: "Melanoma detection", and Code number: LS7 (52), within the framework of «Supporting Postdoctoral Researchers» of the Operational Program "Education  ...  and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State and the scan4reco funded H2020  ...  Schematic of the biosensor construction A biosensor, according to IUPAC, is "a device that uses specific biochemical reactions mediated by isolated enzymes, immunosystems, tissues, organelles or whole  ... 
doi:10.5281/zenodo.3679293 fatcat:73khu2juijho3ghrorov4wsur4

SU-FF-I-79: Java-Based Plugin for Tomographic Reconstruction for SPECT Data

M Andrade, M Costa, A Marques da Silva
2006 Medical Physics (Lancaster)  
True alignment of planning and on-board image data was achieved according to a 3D point-based registration of the 6 reference BBs in the CT and CBCT images.  ...  This study examines the accuracy of 3D-3D registration of reference and on-board DTS images to assess the potential of DTS for image-guided radiation therapy (IGRT).  ...  With 6 clusters, a cluster of grey matter would appear with more tissue homogeneity and less partial volume effect which would result 20-25% increase in average measured signal in that whole segment which  ... 
doi:10.1118/1.2240759 fatcat:5we2nvgwx5hrxcv2ngufce6obq
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