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

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.

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Impact of respiratory motion on breast tangential radiotherapy using the field-in-field technique compared to irradiation using physical wedges

Radiology and Oncology ◽

10.2478/raon-2013-0032 ◽

2014 ◽

Vol 48 (1) ◽

pp. 94-98 ◽

Cited By ~ 6

Author(s):

Hidekazu Tanaka ◽

Shinya Hayashi ◽

Kazuhiro Ohtakara ◽

Hiroaki Hoshi

Keyword(s):

Respiratory Motion ◽

Early Stage ◽

Free Breathing ◽

Ct Images ◽

Data Set ◽

Acceptable Range ◽

Treatment Plans ◽

Prescription Dose ◽

Ct Data ◽

The Impact

Abstract Background. This study aimed to evaluate whether the field-in-field (FIF) technique was more vulnerable to the impact of respiratory motion than irradiation using physical wedges (PWs). Patients and methods. Ten patients with early stage breast cancer were enrolled. Computed tomography (CT) was performed during free breathing (FB). After the FB-CT data set acquisition, 2 additional CT scans were obtained during a held breath after light inhalation (IN) and light exhalation (EX). Based on the FB-CT images, 2 different treatment plans were created for the entire breast for each patient and copied to the IN-CT and EX-CT images. The amount of change in the volume of the target receiving 107%, 95%, and 90% of the prescription dose (V107%, V95%, and V90%, respectively), on the IN-plan and EX-plan compared with the FB-plan were evaluated. Results. The V107%, V95%, and V90% were significantly larger for the IN-plan than for the FB-plan in both the FIF technique and PW technique. While the amount of change in the V107% was significantly smaller in the FIF than in the PW plan, the amount of change in the V95% and V90% was significantly larger in the FIF plan. Thus, the increase in the V107% was smaller while the increases in the V95% and V90% were larger in the FIF than in the PW plan. Conclusions. During respiratory motion, the dose parameters stay within acceptable range irrespective of irradiation technique used although the amount of change in dose parameters was smaller with FIF technique.

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

10.32920/ryerson.14653362 ◽

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

10.32920/ryerson.14653362.v1 ◽

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

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Fully Automatic Segmentation and Landmarking of Hip CT Images

10.29007/ds5r ◽

2020 ◽

Author(s):

Guoyan Zheng

Keyword(s):

Automatic Segmentation ◽

Ct Images ◽

Generic Models ◽

Mean Error ◽

Segmentation Accuracy ◽

Fully Automatic ◽

Ct Data ◽

Two Stages ◽

Atlas Segmentation ◽

Acetabular Rim

We present a fully automatic method of segmenting and landmarking hip CT images for planning of Total Hip Arthroplasty (THA). Our method consists of two stages, i.e., the segmentation stage and the landmarking stage. At the segmentation stage, a multi-atlas segmentation constrained graph method is employed to fully automatically segment both the pelvis and the bilateral proximal femurs from the input CT data. The segmentation stage is followed by the landmarking stage, where a set of pre-defined landmarks are transferred from generic models of the associated hip structures to the input CT space via non-rigid registrations in order to compute a set of functional parameters that are relevant to planning of THA. Evaluated on 20 hip patients, we computed both the segmentation accuracy and the landmarking accuracy. An average segmentation error of 0.38 ± 0.25 mm and 0.49 ± 0.22 mm was found for the hemi-pelvis and for the proximal femurs, respectively. For 3D landmarking, a mean error of 1.58 ± 0.87 mm and 0.46 ± 0.39 mm was found for the acetabular rim center and the acetabular rim radius, respectively; a mean error of 0.74±0.45o was found for the orientation of the anterior pelvic plane; and a mean error of 3.14 ± 1.90 mm and 2.04 ± 1.61 mm was found for the femoral head center and the femoral offset, respectively.

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