Overview of the CLEF 2009 Medical Image Annotation Track

Tommasi, Tatiana; Caputo, Barbara; Welter, Petra; Güld, Mark Oliver; Deserno, Thomas M.

doi:10.1007/978-3-642-15751-6_9

Cited by 48 publications

(31 citation statements)

References 6 publications

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“…The dataset adopted for evaluation was distributed at the ImageCLEF 2009 Medical Annotation task [20, 21]. The training set consists of 12,671 grayscale images and the official evaluation set has 1,732 grayscale images.…”

Section: Methodsmentioning

confidence: 99%

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

2018

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The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes.

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Section: Methodsmentioning

confidence: 99%

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

2018

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“…The database consisted of 10,000 radiographs fully annotated with IRMA code, taken randomly from medical routine. Between 2006 and 2009, ImageCLEFmed kept these two tasks in similar formats format but using larger and more complex databases each year [28][29][30][31][32]. From 2008 to 2010, the database contained images from articles published in Radiology and Radiographics including the text of the captions and a link to the html of the full text articles.…”

Section: Amia: the Medical Taskmentioning

confidence: 99%

ImageCLEF 2013: The Vision, the Data and the Open Challenges

Caputo

Müller

Thomée

et al. 2013

Lecture Notes in Computer Science

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Abstract. This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. The paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities.

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“…Numerous tasks have been proposed in the tracks in [1]. The tasks are focused on classifying medical images to different categories of acquisition modality (CT, X-ray, MR, etc.…”

Section: Related Workmentioning

confidence: 99%

Automatic labeling and classification of brain CT images

Gong

Wang

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Automatic medical image classification is difficult because of the lacking of training data. As manual labeling is too costly, we provide an automatic labeling solution to this problem by making use of the radiology report associated with the medical images. We first segment and reconstruct the 3D regions of interest (ROIs) from the medical images, and extract pathology and anatomy information from the associated report. We use an anatomical atlas to map the ROIs to the anatomy part(s) and match the pathology information of the same anatomy part(s) from the text. In this way, the ROIs are automatically labeled with pathology types which can be served as class labels, and a training data set of a large number of training instances is generated automatically. We extract the volume, color, location, and shape features of the ROIs, and classify the types of ROIs using these features. The overall evaluation result is promising to doctors and medical professionals. Our experiment is conducted using traumatic brain injury CT images; however, our framework of automatically labeling and classifying medical cases can be extended to medical images in other modality or of other anatomical part.

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Overview of the CLEF 2009 Medical Image Annotation Track

Cited by 48 publications

References 6 publications

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

ImageCLEF 2013: The Vision, the Data and the Open Challenges

Automatic labeling and classification of brain CT images

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