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Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis.

Iakovidis DK, Goudas T, Smailis C, Maglogiannis I - ScientificWorldJournal (2014)

Bottom Line: In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system.However a tool for detecting and quantifying the disease is not yet available.The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, 35100 Lamia, Greece.

ABSTRACT
Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

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Related in: MedlinePlus

Salient objects in kidney biopsy images. The arrows indicate the regions of interest. (a) Normal biopsy: (1) nonpathogenic glomerulus; (2) nonpathogenic tubulus. (b) Pathogenic biopsy: (3) pathogenic glomerulus; (4) pathogenic tubulus.
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fig1: Salient objects in kidney biopsy images. The arrows indicate the regions of interest. (a) Normal biopsy: (1) nonpathogenic glomerulus; (2) nonpathogenic tubulus. (b) Pathogenic biopsy: (3) pathogenic glomerulus; (4) pathogenic tubulus.

Mentions: In this work we focus on a methodology that can turn Ratsnake into a fully functional CAD system. The comparative advantage of this approach is that it enables faster development of such systems as plugin modules that can exploit Ratsnake's segmentation, semantic annotation, ontological inference, and measurement capabilities that have been introduced in its latest version. To this end we present a novel application and case study, which can also be considered as a model for developing future CAD systems based on Ratsnake. The CAD system presented in this paper aims at fast evaluation of microscopy images from kidney biopsies. These images are very complex, in the sense that, unlike other types of medical images, their content is characterized by diverse, inhomogeneous regions, densely, not a priori distributed over the image space (Figure 1). A machine learning algorithm has been incorporated to include prior knowledge about the imaging domain of kidney biopsies within the customizable snake model and generate an image force field evaluating textural image features. This force field can be considered as a saliency map derived from the classified image samples, roughly indicating boundaries of ROIs, which guides the snake model to finely segment and automatically annotate these ROIs.


Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis.

Iakovidis DK, Goudas T, Smailis C, Maglogiannis I - ScientificWorldJournal (2014)

Salient objects in kidney biopsy images. The arrows indicate the regions of interest. (a) Normal biopsy: (1) nonpathogenic glomerulus; (2) nonpathogenic tubulus. (b) Pathogenic biopsy: (3) pathogenic glomerulus; (4) pathogenic tubulus.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3926425&req=5

fig1: Salient objects in kidney biopsy images. The arrows indicate the regions of interest. (a) Normal biopsy: (1) nonpathogenic glomerulus; (2) nonpathogenic tubulus. (b) Pathogenic biopsy: (3) pathogenic glomerulus; (4) pathogenic tubulus.
Mentions: In this work we focus on a methodology that can turn Ratsnake into a fully functional CAD system. The comparative advantage of this approach is that it enables faster development of such systems as plugin modules that can exploit Ratsnake's segmentation, semantic annotation, ontological inference, and measurement capabilities that have been introduced in its latest version. To this end we present a novel application and case study, which can also be considered as a model for developing future CAD systems based on Ratsnake. The CAD system presented in this paper aims at fast evaluation of microscopy images from kidney biopsies. These images are very complex, in the sense that, unlike other types of medical images, their content is characterized by diverse, inhomogeneous regions, densely, not a priori distributed over the image space (Figure 1). A machine learning algorithm has been incorporated to include prior knowledge about the imaging domain of kidney biopsies within the customizable snake model and generate an image force field evaluating textural image features. This force field can be considered as a saliency map derived from the classified image samples, roughly indicating boundaries of ROIs, which guides the snake model to finely segment and automatically annotate these ROIs.

Bottom Line: In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system.However a tool for detecting and quantifying the disease is not yet available.The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, 35100 Lamia, Greece.

ABSTRACT
Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

Show MeSH
Related in: MedlinePlus