Automatic multi-label annotation of abdominal CT images using CBIR

Xue, Zhiyun; Long, L. Rodney; Thoma, George R.

doi:10.1117/12.2254368

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Recently, multi-label image annotation has achieved great progress in different domains such as multi-object recognition [21], [22], scene recognition [20], facial action detection [19], and medical diagnostic prediction [17], [18]. In an image annotation task, there is usually more than one label to annotate an image with, and these labels provide significant information such as location, features and events.…”

Section: Introductionmentioning

confidence: 99%

Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization

Mojoo

Zhao

Kavitha

et al. 2020

IEICE Trans. Inf. & Syst.

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The task of image annotation is becoming enormously important for efficient image retrieval from the web and other large databases. However, huge semantic information and complex dependency of labels on an image make the task challenging. Hence determining the semantic similarity between multiple labels on an image is useful to understand any incomplete label assignment for image retrieval. This work proposes a novel method to solve the problem of multi-label image annotation by unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance. The unified Laplacian regularization model is implemented to address the missing labels efficiently by generating the contextual similarity between labels both internally and externally through their semantic similarities, which is the main contribution of this study. Specifically, we generate similarity matrices between labels internally by using Hayashi's quantification method-type III and externally by using the word2vec method. The generated similarity matrices from the two different methods are then combined as a Laplacian regularization term, which is used as the new objective function of the deep CNN. The Regularization term implemented in this study is able to address the multi-label annotation problem, enabling a more effectively trained neural network. Experimental results on public benchmark datasets reveal that the proposed unified regularization model with deep CNN produces significantly better results than the baseline CNN without regularization and other state-of-the-art methods for predicting missing labels.

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Section: Introductionmentioning

confidence: 99%

Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization

Mojoo

Zhao

Kavitha

et al. 2020

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…That model was trained on a bigger training set and does not provide any kind of explanation. The closest method to ours, which was presented in [39], does not give any accuracy as a baseline. Its drawback is that it can miss labels, which happens at least once every five examples.…”

Section: Resultsmentioning

confidence: 97%

“…The task is to perform explained multiple organ annotation by learning a model from few data. While multiple organ detection has been a regularly tackled topic in the literature [36,10,32,24], multiple organ annotation has only been tackled in [39]. The principle of this method is to find images in the dataset that share visual characteristics with the image under study, and then to label it based on the labels from visually similar images.…”

Section: Case Studymentioning

confidence: 99%

A New Approach for Explainable Multiple Organ Annotation with Few Data

Pierrard,

Poli,

Hudelot

2019

Preprint

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Despite the recent successes of deep learning, such models are still far from some human abilities like learning from few examples, reasoning and explaining decisions. In this paper, we focus on organ annotation in medical images and we introduce a reasoning framework that is based on learning fuzzy relations on a small dataset for generating explanations. Given a catalogue of relations, it efficiently induces the most relevant relations and combines them for building constraints in order to both solve the organ annotation task and generate explanations. We test our approach on a publicly available dataset of medical images where several organs are already segmented. A demonstration of our model is proposed with an example of explained annotations. It was trained on a small training set containing as few as a couple of examples.

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“…This multi-structure segmentation feature may also be useful to differentiate HU characteristics in muscle, liver, and cancer tissues, or to assess interpatient HU discrepancies [44]. Moreover, this automatic segmentation feature can also be used for computer-based image context-driven annotation of cardiac CTA image dataset, such as a similar work performed on abdominal CT for multi-label image annotation [45].…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

et al. 2020

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Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application. INDEX TERMS Computed tomography, heart segmentation, multi-atlas segmentation, random walk.

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Automatic multi-label annotation of abdominal CT images using CBIR

Cited by 3 publications

References 6 publications

Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization

Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization

A New Approach for Explainable Multiple Organ Annotation with Few Data

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

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