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Kabir MM, Hafiz MS, Bandyopadhyaa S, Jim JR, Mridha MF. Tea leaf age quality: Age-stratified tea leaf quality classification dataset. Data Brief 2024; 54:110462. [PMID: 38711743 PMCID: PMC11070690 DOI: 10.1016/j.dib.2024.110462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/21/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
The "Tea Leaf Age Quality" dataset represents a pioneering agricultural and machine-learning resource to enhance tea leaf classification, detection, and quality prediction based on leaf age. This comprehensive collection includes 2208 raw images from the historic Malnicherra Tea Garden in Sylhet and two other gardens from Sreemangal and Moulvibajar in Bangladesh. The dataset is systematically categorized into four distinct classes (T1: 1-2 days, T2: 3-4 days, T3: 5-7 days, and T4: 7+ days) according to age-based quality criteria. This dataset helps to determine how tea quality changes with age. The most recently harvested leaves (T1) exhibited superior quality, whereas the older leaves (T4) were suboptimal for brewing purposes. It includes raw, unannotated images that capture the natural diversity of tea leaves, precisely annotated versions for targeted analysis, and augmented data to facilitate advanced research. The compilation process involved extensive on-ground data collection and expert consultations to ensure the authenticity and applicability of the dataset. The "Tea Leaf Age Quality" dataset is a crucial tool for advancing deep learning models in tea leaf classification and quality assessment, ultimately contributing to the technological evolution of the agricultural sector by providing detailed age-stratified tea leaf categorization.
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Affiliation(s)
- Md Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md Sadman Hafiz
- Institute of Information and Communication Technology, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh
| | - Shattik Bandyopadhyaa
- Institute of Information and Communication Technology, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh
| | - Jamin Rahman Jim
- Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
| | - M F Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
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2
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Gut D, Trombini M, Kucybała I, Krupa K, Rozynek M, Dellepiane S, Tabor Z, Wojciechowski W. Use of superpixels for improvement of inter-rater and intra-rater reliability during annotation of medical images. Med Image Anal 2024; 94:103141. [PMID: 38489896 DOI: 10.1016/j.media.2024.103141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/29/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
In the context of automatic medical image segmentation based on statistical learning, raters' variability of ground truth segmentations in training datasets is a widely recognized issue. Indeed, the reference information is provided by experts but bias due to their knowledge may affect the quality of the ground truth data, thus hindering creation of robust and reliable datasets employed in segmentation, classification or detection tasks. In such a framework, automatic medical image segmentation would significantly benefit from utilizing some form of presegmentation during training data preparation process, which could lower the impact of experts' knowledge and reduce time-consuming labeling efforts. The present manuscript proposes a superpixels-driven procedure for annotating medical images. Three different superpixeling methods with two different number of superpixels were evaluated on three different medical segmentation tasks and compared with manual annotations. Within the superpixels-based annotation procedure medical experts interactively select superpixels of interest, apply manual corrections, when necessary, and then the accuracy of the annotations, the time needed to prepare them, and the number of manual corrections are assessed. In this study, it is proven that the proposed procedure reduces inter- and intra-rater variability leading to more reliable annotations datasets which, in turn, may be beneficial for the development of more robust classification or segmentation models. In addition, the proposed approach reduces time needed to prepare the annotations.
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Affiliation(s)
- Daniel Gut
- Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland.
| | - Marco Trombini
- Department of Electric, Electronic, and Telecommunication Engineering and Naval Architecture - DITEN, Università degli Studi di Genova, Via all'Opera Pia 11, 16145 Genoa, Italy
| | - Iwona Kucybała
- Department of Radiology, Jagiellonian University Medical College, ul. Kopernika 19, 31-501 Krakow, Poland
| | - Kamil Krupa
- Department of Radiology, Jagiellonian University Medical College, ul. Kopernika 19, 31-501 Krakow, Poland
| | - Miłosz Rozynek
- Department of Radiology, Jagiellonian University Medical College, ul. Kopernika 19, 31-501 Krakow, Poland
| | - Silvana Dellepiane
- Department of Electric, Electronic, and Telecommunication Engineering and Naval Architecture - DITEN, Università degli Studi di Genova, Via all'Opera Pia 11, 16145 Genoa, Italy
| | - Zbisław Tabor
- Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland
| | - Wadim Wojciechowski
- Department of Radiology, Jagiellonian University Medical College, ul. Kopernika 19, 31-501 Krakow, Poland
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3
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C Pereira S, Mendonça AM, Campilho A, Sousa P, Teixeira Lopes C. Automated image label extraction from radiology reports - A review. Artif Intell Med 2024; 149:102814. [PMID: 38462277 DOI: 10.1016/j.artmed.2024.102814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/29/2023] [Accepted: 02/12/2024] [Indexed: 03/12/2024]
Abstract
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones.
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Affiliation(s)
- Sofia C Pereira
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
| | - Ana Maria Mendonça
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
| | - Aurélio Campilho
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
| | - Pedro Sousa
- Hospital Center of Vila Nova de Gaia/Espinho, Portugal.
| | - Carla Teixeira Lopes
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
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Laruelle E, Palauqui JC, Andrey P, Trubuil A. TreeJ: an ImageJ plugin for interactive cell lineage reconstruction from static images. Plant Methods 2023; 19:128. [PMID: 37974271 PMCID: PMC10655406 DOI: 10.1186/s13007-023-01106-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/08/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND With the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. However, some tissues need to be fixed to be accessible or to improve the staining. In this case, classical software do not offer the possibility of generating any lineage. Because of their rigid cell walls, plants present the advantage of keeping traces of the cell division history over successive generations in the cell patterns. To record this information despite having only a static image, dedicated tools are required. RESULTS We developed an interface to assist users in the building and editing of a lineage tree from a 3D labeled image. Each cell within the tree can be tagged. From the created tree, cells of a sub-tree or cells sharing the same tag can be extracted. The tree can be exported in a format compatible with dedicated software for advanced graph visualization and manipulation. CONCLUSIONS The TreeJ plugin for ImageJ/Fiji allows the user to generate and manipulate a lineage tree structure. The tree is compatible with other software to analyze the tree organization at the graphical level and at the cell pattern level. The code source is available at https://github.com/L-EL/TreeJ .
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Affiliation(s)
- Elise Laruelle
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), Route de Saint Cyr, 78000, Versailles, France.
- MaIAGE, INRAE, Université Paris-Saclay, Domaine de Vilvert, 78350, Jouy-en-josas, France.
- Sainsbury Laboratory, Cambridge University, Bateman Street, CB2 1LR, Cambridge, UK.
| | - Jean-Christophe Palauqui
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), Route de Saint Cyr, 78000, Versailles, France
| | - Philippe Andrey
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), Route de Saint Cyr, 78000, Versailles, France
| | - Alain Trubuil
- MaIAGE, INRAE, Université Paris-Saclay, Domaine de Vilvert, 78350, Jouy-en-josas, France
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Deng R, Li Y, Li P, Wang J, Remedios LW, Agzamkhodjaev S, Asad Z, Liu Q, Cui C, Wang Y, Wang Y, Tang Y, Yang H, Huo Y. Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning. Med Image Comput Comput Assist Interv 2023; 14225:497-507. [PMID: 38529367 PMCID: PMC10961594 DOI: 10.1007/978-3-031-43987-2_48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL.
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Affiliation(s)
| | - Yanwei Li
- Vanderbilt University, Nashville TN 37215, USA
| | - Peize Li
- Vanderbilt University, Nashville TN 37215, USA
| | | | | | | | - Zuhayr Asad
- Vanderbilt University, Nashville TN 37215, USA
| | - Quan Liu
- Vanderbilt University, Nashville TN 37215, USA
| | - Can Cui
- Vanderbilt University, Nashville TN 37215, USA
| | - Yaohong Wang
- Vanderbilt University Medical Center, Nashville TN 37232, USA
| | - Yihan Wang
- Vanderbilt University Medical Center, Nashville TN 37232, USA
| | - Yucheng Tang
- NVIDIA Corporation, Santa Clara and Bethesda, USA
| | - Haichun Yang
- Vanderbilt University Medical Center, Nashville TN 37232, USA
| | - Yuankai Huo
- Vanderbilt University, Nashville TN 37215, USA
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6
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Parks K, Liu X, Reasat T, Khera Z, Baker LX, Chen H, Dawant BM, Saknite I, Tkaczyk ER. Non-Expert Markings of Active Chronic Graft-Versus-Host Disease Photographs: Optimal Metrics of Training Effects. J Digit Imaging 2023; 36:373-378. [PMID: 36344635 PMCID: PMC9984572 DOI: 10.1007/s10278-022-00730-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/09/2022] Open
Abstract
Lack of reliable measures of cutaneous chronic graft-versus-host disease (cGVHD) remains a significant challenge. Non-expert assistance in marking photographs of active disease could aid the development of automated segmentation algorithms, but validated metrics to evaluate training effects are lacking. We studied absolute and relative error of marked body surface area (BSA), redness, and the Dice index as potential metrics of non-expert improvement. Three non-experts underwent an extensive training program led by a board-certified dermatologist to mark cGVHD in photographs. At the end of the 4-month training, the dermatologist confirmed that each trainee had learned to accurately mark cGVHD. The trainees' inter- and intra-rater intraclass correlation coefficient estimates were "substantial" to "almost perfect" for both BSA and total redness. For fifteen 3D photos of patients with cGVHD, the trainees' median absolute (relative) BSA error compared to expert marking dropped from 20 cm2 (29%) pre-training to 14 cm2 (24%) post-training. Total redness error decreased from 122 a*·cm2 (26%) to 95 a*·cm2 (21%). By contrast, median Dice index did not reflect improvement (0.76 to 0.75). Both absolute and relative BSA and redness errors similarly and stably reflected improvements from this training program, which the Dice index failed to capture.
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Affiliation(s)
- Kelsey Parks
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiaoqi Liu
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Tahsin Reasat
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Zain Khera
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura X Baker
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benoit M Dawant
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Inga Saknite
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
| | - Eric R Tkaczyk
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA.
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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7
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Ebenezer S S, Tripuraribhatla R. Exponential Sailfish Optimizer-based generative adversarial network for image annotation on natural scene images. Gene Expr Patterns 2022; 46:119279. [PMID: 36195309 DOI: 10.1016/j.gep.2022.119279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 09/12/2022] [Accepted: 09/28/2022] [Indexed: 11/04/2022]
Abstract
Generally, automatic image annotation can offer semantic graphics for recognizing image contents, and it creates a base for devising various techniques, which can search images in a huge dataset. Although most existing techniques mainly focus on resolving annotation issues through sculpting tag semantic information and visual image content, it ignores additional information, like picture positions and descriptions. The established Exponential Sailfish Optimizer-based Generative Adversarial Networks are therefore used to provide an efficient approach for image annotation (ESFO-based GAN). By combining Exponentially Weighted Moving Average (EWMA) and Sailfish Optimizer (SFO), the ESFO is a newly created design that is used to train the GAN classifier. Additionally, the Grabcut is presented to successfully do image annotation by extracting the background and foreground images. Additionally, DeepJoint segmentation is used to divide apart the images based on the background image that was extracted. Finally, image annotation is successfully accomplished with the aid of GAN. As a result, image annotation uses the produced ESFO-based GAN's subsequent results. The developed approach exhibited enhanced outcomes with maximum F-Measure of 98.37%, maximum precision of 97.02%, and maximal recall of 96.64%, respectively, using the flicker dataset.
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8
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Zhuang M, Chen Z, Wang H, Tang H, He J, Qin B, Yang Y, Jin X, Yu M, Jin B, Li T, Kettunen L. AnatomySketch: An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development. J Digit Imaging 2022; 35:1623-1633. [PMID: 35768752 DOI: 10.1007/s10278-022-00660-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 05/07/2022] [Accepted: 05/18/2022] [Indexed: 11/25/2022] Open
Abstract
The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human-computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software .
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Affiliation(s)
- Mingrui Zhuang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Zhonghua Chen
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
- Faculty of Information Technology, University of Jyväskylä, 40100, Jyväskylä, Finland
| | - Hongkai Wang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.
- Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, 116024, China.
| | - Hong Tang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Jiang He
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Bobo Qin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Yuxin Yang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Xiaoxian Jin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Mengzhu Yu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Baitao Jin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Taijing Li
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Lauri Kettunen
- Faculty of Information Technology, University of Jyväskylä, 40100, Jyväskylä, Finland
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9
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Dong Q, Luo G, Haynor D, O'Reilly M, Linnau K, Yaniv Z, Jarvik JG, Cross N. DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation. J Digit Imaging 2021; 33:1514-1526. [PMID: 32666365 DOI: 10.1007/s10278-020-00370-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Modern, supervised machine learning approaches to medical image classification, image segmentation, and object detection usually require many annotated images. As manual annotation is usually labor-intensive and time-consuming, a well-designed software program can aid and expedite the annotation process. Ideally, this program should be configurable for various annotation tasks, enable efficient placement of several types of annotations on an image or a region of an image, attribute annotations to individual annotators, and be able to display Digital Imaging and Communications in Medicine (DICOM)-formatted images. No current open-source software program fulfills these requirements. To fill this gap, we developed DicomAnnotator, a configurable open-source software program for DICOM image annotation. This program fulfills the above requirements and provides user-friendly features to aid the annotation process. In this paper, we present the design and implementation of DicomAnnotator. Using spine image annotation as a test case, our evaluation showed that annotators with various backgrounds can use DicomAnnotator to annotate DICOM images efficiently. DicomAnnotator is freely available at https://github.com/UW-CLEAR-Center/DICOM-Annotator under the GPLv3 license.
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Affiliation(s)
- Qifei Dong
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA
| | - Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA
| | - David Haynor
- Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA
| | - Michael O'Reilly
- Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA
| | - Ken Linnau
- Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA
| | - Ziv Yaniv
- Medical Science & Computing, LLC, Rockville, MD, 20852, USA.,National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Jeffrey G Jarvik
- Departments of Radiology, Neurological Surgery and Health Services, University of Washington, Seattle, WA, 98104-2499, USA
| | - Nathan Cross
- Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA.
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10
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Bafti SM, Ang CS, Hossain MM, Marcelli G, Alemany-Fornes M, Tsaousis AD. A crowdsourcing semi-automatic image segmentation platform for cell biology. Comput Biol Med 2021; 130:104204. [PMID: 33429139 DOI: 10.1016/j.compbiomed.2020.104204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 12/21/2022]
Abstract
State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowdsourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation's cost (time, click, interaction, etc.) while preserving or even improving the annotation's quality. The annotation quality of non-experts has been investigated using IoU (intersection over union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.
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Affiliation(s)
- Saber Mirzaee Bafti
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK.
| | - Chee Siang Ang
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK
| | - Md Moinul Hossain
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK
| | - Gianluca Marcelli
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK
| | - Marc Alemany-Fornes
- Laboratory of Molecular & Evolutionary Parasitology, RAPID Group, School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
| | - Anastasios D Tsaousis
- Laboratory of Molecular & Evolutionary Parasitology, RAPID Group, School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
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11
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Tabassum S, Ullah S, Al-nur NH, Shatabda S. Poribohon-BD: Bangladeshi local vehicle image dataset with annotation for classification. Data Brief 2020; 33:106465. [PMID: 33195776 PMCID: PMC7642820 DOI: 10.1016/j.dib.2020.106465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/23/2020] [Accepted: 10/23/2020] [Indexed: 12/01/2022] Open
Abstract
Vehicle Classification has become tremendously important due to various applications such as traffic video surveillance, accident avoidance, traffic congestion prevention, bringing intelligent transportation systems. This article presents 'Poribohon-BD' dataset for vehicle classification purposes in Bangladesh. The vehicle images are collected from two sources: i) smartphone camera, ii) social media. The dataset contains 9058 labeled and annotated images of 15 native Bangladeshi vehicles such as bus, motorbike, three-wheeler rickshaw, truck, wheelbarrow. Data augmentation techniques have been applied to keep the number of images comparable to each type of vehicle. For labeling the images, LabelImg tool by Tzuta Lin has been used. Human faces have also been blurred to maintain privacy and confidentiality. The dataset is compatible with various CNN architectures such as YOLO, VGG-16, R-CNN, DPM. It is available for research purposes at https://data.mendeley.com/datasets/pwyyg8zmk5/2.
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Affiliation(s)
- Shaira Tabassum
- Department of Computer Science and Engineering, United International University, Bangladesh
| | - Sabbir Ullah
- Department of Computer Science and Engineering, United International University, Bangladesh
| | - Nakib Hossain Al-nur
- Department of Computer Science and Engineering, United International University, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Bangladesh
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Sudars K, Jasko J, Namatevs I, Ozola L, Badaukis N. Dataset of annotated food crops and weed images for robotic computer vision control. Data Brief 2020; 31:105833. [PMID: 32577458 PMCID: PMC7305380 DOI: 10.1016/j.dib.2020.105833] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/03/2020] [Accepted: 06/03/2020] [Indexed: 12/02/2022] Open
Abstract
Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages
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Affiliation(s)
- Kaspars Sudars
- Institute of Electronics and Computer Science, Dzērbenes str.14, Riga LV-1006, Latvia
| | - Janis Jasko
- Institute for Plant Protection Research `Agrihorts', Latvia University of Life Sciences and Technologies, P. Lejiņa str. 2, LV-3004 Jelgava, Latvia
| | - Ivars Namatevs
- Institute of Electronics and Computer Science, Dzērbenes str.14, Riga LV-1006, Latvia
| | - Liva Ozola
- Institute of Electronics and Computer Science, Dzērbenes str.14, Riga LV-1006, Latvia
| | - Niks Badaukis
- Institute for Plant Protection Research `Agrihorts', Latvia University of Life Sciences and Technologies, P. Lejiņa str. 2, LV-3004 Jelgava, Latvia
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Pedrosa M, Silva JM, Silva JF, Matos S, Costa C. SCREEN-DR: Collaborative platform for diabetic retinopathy. Int J Med Inform 2018; 120:137-146. [PMID: 30409338 DOI: 10.1016/j.ijmedinf.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 09/25/2018] [Accepted: 10/14/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND AND OBJECTIVE Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection. This article presents a multidisciplinary collaborative platform that was created to meet the needs of physicians and researchers, aiming at the creation of machine learning algorithms to facilitate the screening process. METHODS Our proposal is a collaborative platform for textual and visual annotation of image datasets. The architecture and layout were optimized for annotating DR images by gathering feedback from several physicians during the design and conceptualization of the platform. It allows the aggregation and indexing of imagiology studies from diverse sources, and supports the creation and annotation of phenotype-specific datasets to feed artificial intelligence algorithms. The platform makes use of an anonymization pipeline and role-based access control for securing personal data. RESULTS The SCREEN-DR platform has been deployed in the production environment of the SCREEN-DR project at http://demo.dicoogle.com/screen-dr, and the source code of the project is publicly available. We provide a description of the platform's interface and use cases it supports. At the time of publication, four physicians have created a total of 1826 annotations for 701 distinct images, and the annotated data has been used for training classification models.
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Mercy Rajaselvi Beaulah P, Manjula D, Sugumaran V. Categorization of Images Using Autoencoder Hashing and Training of Intra Bin Classifiers for Image Classification and Annotation. J Med Syst 2018; 42:132. [PMID: 29892911 DOI: 10.1007/s10916-018-0986-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 06/01/2018] [Indexed: 11/26/2022]
Abstract
Automatic annotation of images is considered to be an important research problem in image retrieval. Traditional methods are computationally complex and fail to annotate correctly when the number of image classes is large and related. This paper proposes a novel approach, an autoencoder hashing, to categorize images of large-scale image classes. The intra bin classifiers are trained to classify the query image, and the tag weight and tag frequency are computed to achieve a more effective annotation of the query image. The proposed approach has been compared with other existing approaches in the literature using performance measures, such as precision, accuracy, mean average precision (MAP), and F1 score. The experimental results indicate that our proposed approach outperforms the existing approaches.
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Affiliation(s)
| | - D Manjula
- Department of Computer science & Engineering, Anna University, Chennai, India
| | - Vijayan Sugumaran
- Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI, 48309, USA
- Center for Data Science and Big Data Analytics, Oakland University, Rochester, MI, 48309, USA
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Adebayo S, McLeod K, Tudose I, Osumi-Sutherland D, Burdett T, Baldock R, Burger A, Parkinson H. PhenoImageShare: an image annotation and query infrastructure. J Biomed Semantics 2016; 7:35. [PMID: 27267125 PMCID: PMC4896029 DOI: 10.1186/s13326-016-0072-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 05/05/2016] [Indexed: 01/12/2023] Open
Abstract
Background High throughput imaging is now available to many groups and it is possible to generate a large quantity of high quality images quickly. Managing this data, consistently annotating it, or making it available to the community are all challenges that come with these methods. Results PhenoImageShare provides an ontology-enabled lightweight image data query, annotation service and a single point of access backed by a Solr server for programmatic access to an integrated image collection enabling improved community access. PhenoImageShare also provides an easy to use online image annotation tool with functionality to draw regions of interest on images and to annotate them with terms from an autosuggest-enabled ontology-lookup widget. The provenance of each image, and annotation, is kept and links to original resources are provided. The semantic and intuitive search interface is species and imaging technology neutral. PhenoImageShare now provides access to annotation for over 100,000 images for 2 species. Conclusion The PhenoImageShare platform provides underlying infrastructure for both programmatic access and user-facing tools for biologists enabling the query and annotation of federated images. PhenoImageShare is accessible online at http://www.phenoimageshare.org.
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Affiliation(s)
- Solomon Adebayo
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Crewe Road, Edinburgh, UK
| | - Kenneth McLeod
- Department of Computer Science, Heriot-Watt University, Edinburgh, UK
| | - Ilinca Tudose
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Tony Burdett
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Richard Baldock
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Crewe Road, Edinburgh, UK
| | - Albert Burger
- Department of Computer Science, Heriot-Watt University, Edinburgh, UK
| | - Helen Parkinson
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
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Kumar A, Dyer S, Kim J, Li C, Leong PHW, Fulham M, Feng D. Adapting content-based image retrieval techniques for the semantic annotation of medical images. Comput Med Imaging Graph 2016; 49:37-45. [PMID: 26890880 DOI: 10.1016/j.compmedimag.2016.01.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Revised: 12/10/2015] [Accepted: 01/14/2016] [Indexed: 10/22/2022]
Abstract
The automatic annotation of medical images is a prerequisite for building comprehensive semantic archives that can be used to enhance evidence-based diagnosis, physician education, and biomedical research. Annotation also has important applications in the automatic generation of structured radiology reports. Much of the prior research work has focused on annotating images with properties such as the modality of the image, or the biological system or body region being imaged. However, many challenges remain for the annotation of high-level semantic content in medical images (e.g., presence of calcification, vessel obstruction, etc.) due to the difficulty in discovering relationships and associations between low-level image features and high-level semantic concepts. This difficulty is further compounded by the lack of labelled training data. In this paper, we present a method for the automatic semantic annotation of medical images that leverages techniques from content-based image retrieval (CBIR). CBIR is a well-established image search technology that uses quantifiable low-level image features to represent the high-level semantic content depicted in those images. Our method extends CBIR techniques to identify or retrieve a collection of labelled images that have similar low-level features and then uses this collection to determine the best high-level semantic annotations. We demonstrate our annotation method using retrieval via weighted nearest-neighbour retrieval and multi-class classification to show that our approach is viable regardless of the underlying retrieval strategy. We experimentally compared our method with several well-established baseline techniques (classification and regression) and showed that our method achieved the highest accuracy in the annotation of liver computed tomography (CT) images.
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Affiliation(s)
- Ashnil Kumar
- School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia.
| | - Shane Dyer
- School of Electrical and Information Engineering, University of Sydney, Australia.
| | - Jinman Kim
- School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia.
| | - Changyang Li
- School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia.
| | - Philip H W Leong
- School of Electrical and Information Engineering, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia.
| | - Michael Fulham
- School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Australia.
| | - Dagan Feng
- School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiao Tong University, China.
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Lingutla NT, Preece J, Todorovic S, Cooper L, Moore L, Jaiswal P. AISO: Annotation of Image Segments with Ontologies. J Biomed Semantics 2014; 5:50. [PMID: 25584184 PMCID: PMC4290088 DOI: 10.1186/2041-1480-5-50] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Accepted: 11/26/2014] [Indexed: 01/24/2023] Open
Abstract
Background Large quantities of digital images are now generated for biological collections, including those developed in projects premised on the high-throughput screening of genome-phenome experiments. These images often carry annotations on taxonomy and observable features, such as anatomical structures and phenotype variations often recorded in response to the environmental factors under which the organisms were sampled. At present, most of these annotations are described in free text, may involve limited use of non-standard vocabularies, and rarely specify precise coordinates of features on the image plane such that a computer vision algorithm could identify, extract and annotate them. Therefore, researchers and curators need a tool that can identify and demarcate features in an image plane and allow their annotation with semantically contextual ontology terms. Such a tool would generate data useful for inter and intra-specific comparison and encourage the integration of curation standards. In the future, quality annotated image segments may provide training data sets for developing machine learning applications for automated image annotation. Results We developed a novel image segmentation and annotation software application, “Annotation of Image Segments with Ontologies” (AISO). The tool enables researchers and curators to delineate portions of an image into multiple highlighted segments and annotate them with an ontology-based controlled vocabulary. AISO is a freely available Java-based desktop application and runs on multiple platforms. It can be downloaded at http://www.plantontology.org/software/AISO. Conclusions AISO enables curators and researchers to annotate digital images with ontology terms in a manner which ensures the future computational value of the annotated images. We foresee uses for such data-encoded image annotations in biological data mining, machine learning, predictive annotation, semantic inference, and comparative analyses.
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Affiliation(s)
- Nikhil Tej Lingutla
- School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, Corvallis, OR 97331-2902 USA
| | - Justin Preece
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA
| | - Sinisa Todorovic
- School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, Corvallis, OR 97331-2902 USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA
| | - Laura Moore
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA
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Kurtz C, Depeursinge A, Napel S, Beaulieu CF, Rubin DL. On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Med Image Anal 2014; 18:1082-100. [PMID: 25036769 PMCID: PMC4173098 DOI: 10.1016/j.media.2014.06.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Revised: 06/18/2014] [Accepted: 06/23/2014] [Indexed: 10/25/2022]
Abstract
Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. 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. 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 relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.
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Affiliation(s)
- Camille Kurtz
- Department of Radiology, School of Medicine, Stanford University, USA; LIPADE Laboratory (EA 2517), University Paris Descartes, France.
| | | | - Sandy Napel
- Department of Radiology, School of Medicine, Stanford University, USA.
| | | | - Daniel L Rubin
- Department of Radiology, School of Medicine, Stanford University, USA.
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