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On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

Camille Kurtz, Adrien Depeursinge, Sandy Napel, Christopher F. Beaulieu, Daniel L. Rubin
2014 Medical Image Analysis  
We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology.  ...  The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem  ...  This project was funded in part by a Grant from National Cancer Institute, National Institutes of Health (# U01CA142555-01, # R01 CA160251), the Swiss National Science Foundation (# PBGEP2_142283), and  ... 
doi:10.1016/j.media.2014.06.009 pmid:25036769 pmcid:PMC4173098 fatcat:jxg3gipstndjzabu3rpfl5vweu

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Camille Kurtz, Christopher F. Beaulieu, Sandy Napel, Daniel L. Rubin
2014 Journal of Biomedical Informatics  
It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms  ...  We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver.  ...  Acknowledgments The authors would like to thank Jarrett Rosenberg for his useful help on statistical evaluations.  ... 
doi:10.1016/j.jbi.2014.02.018 pmid:24632078 pmcid:PMC4058405 fatcat:movcgiinojdkpoiwemrx4oopai

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database [article]

Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam Harrison, Mohammadhad Bagheri, Ronald Summers
2018 arXiv   pre-print
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.  ...  Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images.  ...  Acknowledgments This research was supported by the Intramural Research Program of the NIH Clinical Center. We thank NVIDIA for the donation of GPU cards.  ... 
arXiv:1711.10535v3 fatcat:gluv6q2cqzf6xlayfk2lcmh7ba

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database

Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam P. Harrison, Mohammadhadi Bagheri, Ronald M. Summers
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.  ...  Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images.  ...  Acknowledgments This research was supported by the Intramural Research Program of the NIH Clinical Center. We thank NVIDIA for the donation of GPU cards.  ... 
doi:10.1109/cvpr.2018.00965 dblp:conf/cvpr/YanWLZHBS18 fatcat:bjjslp4phrdplnpyl75zvnr5ru

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  
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year.  ...  ArXiv was searched for papers mentioning one of a set of terms related to medical imaging.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control

M. Wels, B. M. Kelm, A. Tsymbal, M. Hammon, G. Soza, M. Sühling, A. Cavallaro, D. Comaniciu, Bram van Ginneken, Carol L. Novak
2012 Medical Imaging 2012: Computer-Aided Diagnosis  
In this paper we present a method for fully-automatic osteolytic spinal bone lesion detection from 3D CT data.  ...  Our method achieves a cross-validated sensitivity score of 75% and a mean false positive rate of 3.0 per volume on a data collection consisting of 34 patients with 105 osteolytic spinal bone lesions.  ...  A similar online Random Forest implementation has been recently used in a content-based image retrieval system for liver lesion retrieval and characterization. 15 Post-Processing After having detected  ... 
doi:10.1117/12.911169 dblp:conf/micad/WelsKTHSSCC12 fatcat:b4xxeha2q5crxjgw4c2w2j5lrq

Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging

Kyu-Hwan Jung, Hyunho Park, Woochan Hwang
2017 Hanyang Medical Reviews  
images with respect to the target tasks.  ...  In this paper, we will review recent applications of deep learning in the analysis of CT and MR images in a range of tasks and target organs.  ...  , and similar image retrieval [1] .  ... 
doi:10.7599/hmr.2017.37.2.61 fatcat:f4dl4szy35bhfilas3kyblzgui

Effective of Modern Techniques on Content-Based Medical Image Retrieval: A Survey

Metwally Rashad, Sameer Nooh, Ibrahem Afifi, Mohamed Abdelfatah
2022 International journal of computer science and mobile computing  
The majority of the methods already in use in CBMIR enhance the retrieval of a medical image and diseases diagnosis by reducing the issue of the semantic gap between low visual and high semantic levels  ...  This implies that a precise, efficient way of indexing and retrieving biomedical images is necessary to obtain medical images from such repositories in real-time.  ...  Two methods for retrieval of liver lesions were suggested. In the first method, for every imaging phase, individual features were selected to enhance the lesion class separability.  ... 
doi:10.47760/ijcsmc.2022.v11i03.008 fatcat:656cjypw75h43mjfflbngdouia

DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers
2018 Journal of Medical Imaging  
We mine bookmarks in our institute to develop DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients.  ...  There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications.  ...  Acknowledgments This work was supported by the Intramural Research Program of the National Institutes of Health, Clinical Center.  ... 
doi:10.1117/1.jmi.5.3.036501 pmid:30035154 fatcat:ckzsignyarbdjbcizwqrkhty6u

Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks [article]

Yuanpeng Liu, Qinglei Hui, Zhiyi Peng, Shaolin Gong, Dexing Kong
2021 arXiv   pre-print
Existing automatic segmentation methods are mainly based on fully supervised learning and have an extremely high demand for precise annotations, which are very costly and time-consuming to obtain.  ...  To address this problem, we proposed an automatic CT segmentation method based on weakly supervised learning, by which one could train an accurate segmentation model only with weak annotations in the form  ...  Acknowledgment The authors thank the organizers of the LiTS challenge, KiTS19 challenge, and MSD segmentation challenge for providing CT segmentation data.  ... 
arXiv:2105.14314v3 fatcat:emqsunxbxne2fpptiahyv3mxmi

Immediate ROI Search for 3-D Medical Images [chapter]

Karen Simonyan, Marc Modat, Sebastien Ourselin, David Cash, Antonio Criminisi, Andrew Zisserman
2013 Lecture Notes in Computer Science  
image registration schemes; (iii) we propose a discriminative method for learning to rank the returned images based on the content of the ROI.  ...  The objective of this work is a scalable, real-time, visual search engine for 3-D medical images, where a user is able to select a query Region Of Interest (ROI) and automatically detect the corresponding  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (NIH Grant U01 AG024904).  ... 
doi:10.1007/978-3-642-36678-9_6 fatcat:xy4bwwaybbgh3kdkqtvpayhgpy

Performance benchmarking of liver CT image segmentation and volume estimation

Wei Xiong, Jiayin Zhou, Qi Tian, Jimmy J. Liu, Yingyi Qi, Wee Kheng Leow, Thazin Han, Shih-chang Wang, Katherine P. Andriole, Khan M. Siddiqui
2008 Medical Imaging 2008: PACS and Imaging Informatics  
In the current study, approximately 70 sets of abdominal CT images with normal livers have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of  ...  We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built available to medical imaging research community for performance benchmarking of liver segmentation  ...  ACKNOWLEDGEMENT The research work is supported by the Singapore Bioimaging Consortium (SBIC), Agency for Science Technology and  ... 
doi:10.1117/12.770858 fatcat:zruru6chsvahhotqjkzf75xylm

Region of Interest Queries in CT Scans [chapter]

Alexander Cavallaro, Franz Graf, Hans-Peter Kriegel, Matthias Schubert, Marisa Thoma
2011 Lecture Notes in Computer Science  
To answer ROI queries, our new method employs instance-based regression in combination with interpolation techniques for mapping the slices of a scan to a height model of the human body.  ...  Since our method is based on image similarity, it is very flexible w.r.t. the size and the position of the scanned region.  ...  Acknowledgements The authors would like to thank Sascha Seifert et.al. for providing the executables of [13] and Robert Forbrig for generating the anatomical annotations used in Sect. 6.2.  ... 
doi:10.1007/978-3-642-22922-0_5 fatcat:jdd5ws3g3vcttfp4pmylrnhf4a

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation [article]

Ali Hatamizadeh
2020 arXiv   pre-print
In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework  ...  In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream  ...  The liver with a mean lesion radius of 17.42 ± 9.516 pixels. The lung component consists of 87 CT images with a mean lesion radius of 15.15 ± 5.777 pixels.  ... 
arXiv:2006.12706v1 fatcat:6jchhrv6zrhlhbpcak6fcbh4a4

Annotating Medical Image Data [chapter]

Katharina Grünberg, Oscar Jimenez-del-Toro, Andras Jakab, Georg Langs, Tomàs Salas Fernandez, Marianne Winterstein, Marc-André Weber, Markus Krenn
2017 Cloud-Based Benchmarking of Medical Image Analysis  
The GeoS tool was finally chosen for the annotation based on the detailed analysis, allowing for efficient and effective annotations. 3D slice was chosen for smaller structures with low contrast to complement  ...  A detailed quality control was also installed, including an automatic tool that attributes organs to annotate and volumes to specific annotators, and then compares results.  ...  The VISCERAL project developed a cloud-based infrastructure for the evaluation of detection, analysis and retrieval algorithms on large medical image datasets [8, 9] .  ... 
doi:10.1007/978-3-319-49644-3_4 fatcat:5wvxrqrbf5bwjbdl5kdwiiehii
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