A fully automatic probabilistic 3D approach for the detection and assessment of pleural thickenings from CT data

2013 ◽  
Author(s):  
Kraisorn Chaisaowong ◽  
Chaicharn Akkawutvanich ◽  
Christoph Wilkmann ◽  
Thomas Kraus
Keyword(s):  
Fully Automatic ◽  
Ct Data

scholarly journals Fully-Automatic CT Data Preparation for Interventional X-Ray Skin Dose Simulation

2020 ◽  
pp. 125-130 ◽  
Author(s):  
Philipp Roser ◽  
Annette Birkhold ◽  
Alexander Preuhs ◽  
Bernhard Stimpel ◽  
Christopher Syben ◽  
...  
Keyword(s):  
Skin Dose ◽  
Data Preparation ◽  
X Ray ◽  
Fully Automatic ◽  
Ct Data

scholarly journals Automated incisional hernia characterization by non-rigid registration of CT images – a pilot study

2020 ◽  
Vol 6 (3) ◽  
pp. 91-94
Author(s):  
Samuel Voß ◽  
Philipp D. Lösel ◽  
Vincent Heuveline ◽  
Sylvia Saalfeld ◽  
Philipp Berg ◽  
...  
Keyword(s):  
Incisional Hernia ◽  
Clinical Observation ◽  
Valsalva Maneuver ◽  
Incisional Hernia Repair ◽  
Ct Images ◽  
Software Framework ◽  
Maximum Displacement ◽  
Data Set ◽  
Fully Automatic ◽  
Ct Data

AbstractIncisional hernia repair makes use of prosthetic meshes to re-establish a biomechanically stable abdominal wall. Mesh sizing and fixation have been found to be essential for the clinical outcome. Comparative CT images a) under rest versus b) under Valsalva maneuver (exhalation against closed airways) provide useful information for hernia characterization. However, this process incorporates several manual measurements, which led to observer variability. The present study suggests using an image registration approach of the CT data to reliably and reproducibly extract hernia quantities. The routine is implemented in the software framework MATLAB and works fully automatic. After CT data import, slice by slice undergo non-rigid B-spline grid registration. Local displacement and strain are extracted from the transformation field. The qualitative results correspond to the clinical observation. Maximum displacement of 3.5 cm and maximum strain of 25 % are calculated for one patient’s data set. Current approaches do not provide this type of information. Further research will focus on validation and possibilities to include this new kind of knowledge into the design process of prosthetic meshes.

scholarly journals Multi-object Segmentation of Head Bones

2009 ◽  
Author(s):  
Dagmar Kainmueller ◽  
Hans Lamecker ◽  
Heiko Seim ◽  
Stefan Zachow
Keyword(s):  
Image Data ◽  
Special Focus ◽  
Heuristic Model ◽  
3D Segmentation ◽  
Mandibular Bone ◽  
Shape Models ◽  
Statistical Shape ◽  
Fully Automatic ◽  
Ct Data ◽  
Manual Correction

We present a fully automatic method for 3D segmentation of the mandibular bone from CT data. The method includes an adaptation of statistical shape models of the mandible, the skull base and the midfacial bones, followed by a simultaneous graph-based optimization of adjacent deformable models. The adaptation of the models to the image data is performed according to a heuristic model of the typical intensity distribution in the vincinity of the bone boundary, with special focus on an accurate discrimination of adjacent bones in joint regions. An evaluation of our method based on 18 CT scans shows that a manual correction of the automatic segmentations is not necessary in approx. 60% of the axial slices that contain the mandible.

Research on CT Image Segmentation of Computer-Aided Liver Operation

2014 ◽  
Vol 513-517 ◽  
pp. 3115-3121
Author(s):  
Yun Tao Wei ◽  
Yi Bing Zhou
Keyword(s):  
A Priori ◽  
Liver Segmentation ◽  
Segmentation Methods ◽  
Pros And Cons ◽  
Automatic Methods ◽  
Fully Automatic ◽  
Ct Data ◽  
Liver Region ◽  
The Comparative Study ◽  
Threshold Process

The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. Generally, liver segmentation methods are divided into two main classes, semi-automatic and fully automatic methods, under each of these two categories, several methods, approaches, related issues and problems will be defined and explained. The evaluation measurements and scoring for the liver segmentation are shown, followed by the comparative study for liver segmentation methods, pros and cons of methods will be accentuated carefully. Here a fully 3D algorithm for automatic liver segmentation from CT volumetric datasets is presented. The algorithmstarts by smoothing the original volume using anisotropic diffusion. The coarse liver region is obtained from the threshold process that is based on a priori knowledge.

scholarly journals A Context Based Automated System for Lung Nodule Detection in CT Images

2021 ◽  
Author(s):  
Maciej Dajnowiec
Keyword(s):  
Lung Nodule ◽  
Region Growing ◽  
Ct Images ◽  
Automated System ◽  
Data Sets ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Testing Data ◽  
Fully Automatic ◽  
Ct Data

This thesis is focused on automatic lung nodule detection in CT images. CAD systems are suited for this tak because the sheer volume of information present in CT data sets is overwhelming for radiologists to process. The system developed in this thesis presents a fully automatic solution that applies a sequential algoriths which strongly focuses on nodule context. The system operates at a rate of 80% sensitivity with 3.05 FPs per slice. Our testing data, consisting of 19 CTdata sets containing239 lung nodules, is extremely robust when compared with other documented systems. In addition it introduces many new approaches such as a tight bounding, vessel connectivity, perimeter analysis, adaptive MLT and region growing based lung segmentation. The experimental results produced by this systemare an affirmation of the competitiveness of its performance when compared to other documented approaches.

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