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Author: Tim Johann3
Guided interactive image segmentation using machine learning and color-based image set clustering.

Abstract (Expand)

MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes … in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited. RESULTS: We present a novel approach that combines machine learning-based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general. AVAILABILITY AND IMPLEMENTATION: The presented methods are implemented in our image processing software TiQuant which is freely available at tiquant.hoehme.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors: A. Friebel, T. Johann, D. Drasdo, S. Hoehme

Date Published: 30th Sep 2022

Publication Type: Journal

Guided interactive image segmentation using machine learning and color based data set clustering

Abstract

Not specified

Authors: Adrian Friebel, Tim Johann, Dirk Drasdo, Stefan Hoehme

Date Published: 18th May 2020

Publication Type: Misc

A multilevel framework to reconstruct anatomical 3D models of the hepatic vasculature in rat livers

Abstract (Expand)

The intricate (micro)vascular architecture of the liver has not yet been fully unravelled. Although current models are often idealized simplifications of the complex anatomical reality, correct … morphological information is instrumental for scientific and clinical purposes. Previously, both vascular corrosion casting (VCC) and immunohistochemistry (IHC) have been separately used to study the hepatic vasculature. Nevertheless, these techniques still face a number of challenges such as dual casting in VCC and limited imaging depths for IHC. We have optimized both techniques and combined their complementary strengths to develop a framework for multilevel reconstruction of the hepatic circulation in the rat. The VCC and micro-CT scanning protocol was improved by enabling dual casting, optimizing the contrast agent concentration, and adjusting the viscosity of the resin (PU4ii). IHC was improved with an optimized clearing technique (CUBIC) that extended the imaging depth for confocal microscopy more than five-fold. Using in-house developed software (DeLiver), the vascular network - in both VCC and IHC datasets - was automatically segmented and/or morphologically analysed. Our methodological framework allows 3D reconstruction and quantification of the hepatic circulation, ranging from the major blood vessels down to the intertwined and interconnected sinusoids. We believe that the presented framework will have value beyond studies of the liver, and will facilitate a better understanding of various parenchymal organs in general, in physiological and pathological circumstances.

Authors: Geert Peeters, Charlotte Debbaut, Wim Laleman, Adrian Friebel, Diethard Monbaliu, Ingrid Vander Elst, Jan R Detrez, Tim Vandecasteele, Tim Johann, Thomas De Schryver, Luc Van Hoorebeke, Kasper Favere, Jonas Verbeke, Dirk Drasdo, Stefan Hoehme, Patrick Segers, Pieter Cornillie, Winnok H De Vos

Date Published: 28th Dec 2016

Publication Type: Not specified

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