Stable dynamic 3D shape models

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

Seim

Preim

et al. 2006

Abstract. The quantitative assessment of neck lymph nodes in the context of malign tumors requires an ecient segmentation technique for lymph nodes in tomographic 3D datasets. We present a Stable 3D MassSpring Model for lymph node segmentation in CT datasets. Our model for the rst time represents concurrently the characteristic gray value range, directed contour information as well as shape knowledge, which leads to a much more robust and ecient segmentation process. Our model design and segmentation accuracy are both evaluated with lymph nodes from clinical CT neck datasets.

“…[6] for the neck lymph nodes segmentation, which allow for explicit modelling of shape and appearance, while leaving the modelled object size exible.…”

Section: Methodsmentioning

confidence: 99%

“…Since the model simulation is based on a local optimization process [6], the model should initially be placed inside the target lymph node. Thereto, the user species a seed point, at which a scaled model of minimal size (0.3mm) is placed automatically, assuming that all lymph nodes are at least twice as big as this minimal size.…”

Section: Segmentation Processmentioning

confidence: 99%

Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

Seim

Preim

et al. 2006

“…The quality of fit shall be calculated for Stable Mass Spring Models (SMSMs, [5]), which have already adapted to the data. We will show that this kind of shape model is especially appropriate for this task.…”

Section: Problem Statementmentioning

confidence: 99%

Quality of fit of Stable Mass-Spring Models

Pattern Recognit. Image Anal.

2008

Self Cite

Shape models are used for many tasks in modern image analysis, e.g. segmentation, tracking, etc. Rarely, the quality of fit of adapted shape models is automatically determined to decide, whether the model fitting was successful. This paper develops a principal strategy to measure this quality of fit and defines a specific measure in the case of Stable Mass Spring Models, which are especially appropriate for that. The quality of fit measure is tested in two medical segmentation cases (left ventricles in SPECT data and lymph nodes in CT data) and has been shown to work well. Using application specific thresholds on the quality of fit, it could be automatically detected, whether a segmentation was successful or failed.

“…To overcome these di culties, we have developed a new kind of stable 3D mass spring model that stabilizes itself by the use of an additional internal force, the torsion force ( [7]), even if it is only sparsely interconnected. Stability in traditional models stems from a dense spring meshing that makes them in exible, expensive to compute and di cult to control.…”

Section: Requirementsmentioning

confidence: 99%

Automatic Segmentation of the Left Ventricle in 3D SPECT Data by Registration with a Dynamic Anatomic Model

Lecture Notes in Computer Science

Tönnies

Dixon

2005

Abstract. We present a fully automatic 3D segmentation method for the left ventricle (LV) in human myocardial perfusion SPECT data. This model-based approach consists of 3 phases: 1. nding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour. Finding of the LV is done by exible pattern matching, whereas segmentation is achieved by registering an anatomical model to the functional data. This model is a new kind of stable 3D mass spring model using direction-weighted 3D contour sensors. Our approach is much faster than manual segmention, which is standard in this application up to now. By testing it on 41 LV SPECT datasets of mostly pathological data, we could show, that it is very robust and its results are comparable with those made by human experts.