AI-Supported Interactive Segmentation of 3D Volumes
type: CreativeWork
description: The segmentation of volumetric datasets, i.e., the partitioning of the data into disjoint sub-volumes with the goal to extract information about these regions,is a difficult problem and has been discussed in medical imaging for decades. Due to the ever-increasing imaging capabilities, in particular in X-ray computed tomography (CT) or magnetic resonance imaging, segmentation in industrial applications also gains interest. Especially in industrial applications the generated datasets increase in size. Hence, most applications apply well-known techniques in a 2+1-dimensional manner,i.e., they apply image segmentation procedures on each slice separately and track the progress along the axis of the volume in which the slices are stacked on. This discards the information on preceding or subsequent slices, which is often assumed to be nearly identical. However, in the industrial context this might prove wrong since industrial parts might change their appearance significantly over the course of even a few slices. Moreover, artifacts can further distort the content of the slices. Therefore, three-dimensional processing of voxel volumes has to be preferred, which induces constraints upon the segmentation procedures. For example, they must not consider global information as it is usually not feasible in big scans to compute them efficiently. Yet another frequent problem is that applications focus on individual parts only and algorithms are tailored to that case. Most prominent medical segmentation procedures do so by applying methods to specifically find the liver and only the liver of a patient, for example. The implication is that the same method then cannot be applied to find other parts of the scan and such methods have to be designed individually for any object to be segmented. Flexible segmentation methods are needed too specifically when partitioning unique scans. We define a unique scan to be a voxel dataset for which no comparable volume exists. Classical examples inc…
abstract: The segmentation of volumetric datasets, i.e., the partitioning of the data into disjoint sub-volumes with the goal to extract information about these regions,is a difficult problem and has been discussed in medical imaging for decades. Due to the ever-increasing imaging capabilities, in particular in X-ray computed tomography (CT) or magnetic resonance imaging, segmentation in industrial applications also gains interest. Especially in industrial applications the generated datasets increase in size. Hence, most applications apply well-known techniques in a 2+1-dimensional manner,i.e., they apply image segmentation procedures on each slice separately and track the progress along the axis of the volume in which the slices are stacked on. This discards the information on preceding or subsequent slices, which is often assumed to be nearly identical. However, in the industrial context this might prove wrong since industrial parts might change their appearance significantly over the course of even a few slices. Moreover, artifacts can further distort the content of the slices. Therefore, three-dimensional processing of voxel volumes has to be preferred, which induces constraints upon the segmentation procedures. For example, they must not consider global information as it is usually not feasible in big scans to compute them efficiently. Yet another frequent problem is that applications focus on individual parts only and algorithms are tailored to that case. Most prominent medical segmentation procedures do so by applying methods to specifically find the liver and only the liver of a patient, for example. The implication is that the same method then cannot be applied to find other parts of the scan and such methods have to be designed individually for any object to be segmented. Flexible segmentation methods are needed too specifically when partitioning unique scans. We define a unique scan to be a voxel dataset for which no comparable volume exists. Classical examples inc…
author: Lang, Thomas
keywords: Segmentation, Computed Tomography, Artificial Intelligence, Active Learning, Interactive, Machine learning, Image processing
date published: 2021-07-09
date modified: 2026-07-24T03:45:06Z
provider: Universität Passau
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Universität Passau