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On combining image-based and ontological semantic dissimilarities for medical image retrieval applications
2014
Medical Image Analysis
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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
2014
Journal of Biomedical Informatics
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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]
2018
arXiv
pre-print
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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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
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
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
2012
Medical Imaging 2012: Computer-Aided Diagnosis
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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
2017
Hanyang Medical Reviews
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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
2022
International journal of computer science and mobile computing
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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
2018
Journal of Medical Imaging
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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]
2021
arXiv
pre-print
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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]
2013
Lecture Notes in Computer Science
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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
2008
Medical Imaging 2008: PACS and Imaging Informatics
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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]
2011
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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]
2017
Cloud-Based Benchmarking of Medical Image Analysis
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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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