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Quantification of the ilio-femoral arteries tortuosity and data cluster modelling for preoperative examination

Markiewicz Tomasz, Dziekiewicz Miroslaw

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 4

1123--1136

EN

Detecting spatial tortuosity and atherosclerotic changes of the ilio-femoral arteries are crucial for planning endovascular access. The aim of this study was to find a reliable quantification procedure of arterial lumen and tortuosity to qualify patients for a suitable endovascular procedure. We conducted computed tomographic angiography in 76 patients. All ilio-femoral segments of the arterial tree were visualized using Osirix Dicom Viewer software to help qualify the patients to one of two groups: with possible or non-recommended vascular access. The same tomograms were then analyzed with image processing algorithms to perform ilio-femoral artery segmentation and quantification. We chose a set of arterial tortuosity and lumen measuring methods, such as the modified Gustafson-Kessel clustering algorithm and Support Vector Machine classifier, to automatically classify arterial-tree regions. The two 2D feature spaces were selected with the modified Gustafson-Kessel clusterization to create a combined model to assign around 2/3 cases to the access groups with high specificity (more than 88%) whereas the remaining patients were selected for re-evaluation. We concluded that the novel modification of the Gustafson-Kessel clustering algorithm is more suitable to the highly inseparable data than commonly used approaches. To identify ilio-femoral access limitations, we recommend more complex decision model. This study confirmed high usability of our chosen methodology in the quantitative examination of arteries for endovascular access planning.

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U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images

Les Tomasz

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e137051

EN

This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions.

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Instance Segmentation Model Created from Three Semantic Segmentations of Mask, Boundary and Centroid Pixels Verified on GlaS Dataset

Malík Peter, Krištofík Štefan, Knapová Kristína

Annals of Computer Science and Information Systems

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2020

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Vol. 21

569--576

EN

Segmentation is the key computer vision task in modern medicine applications. Instance segmentation became the prevalent way to improve segmentation performance in recent years. This work proposes a novel way to design an instance segmentation model that combines 3 semantic segmentation models dedicated for foreground, boundary and centroid predictions. It contains no detector so it is orthogonal to a standard instance segmentation design and can be used to improve the performance of a standard design. The presented custom designed model is verified on the Gland Segmentation in Colon Histology Images dataset.

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A fast and robust level set motion-assisted deformable registration method for volumetric CT guided lung intervention

Kim D., Kim N., Lee S., Seo J. B.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

439--447

EN

This paper describes the accurate deformable registration method for image-guided lung interventions, including lung nodule biopsy and radiofrequency ablation of lung tumours. A level set motion assisted deformable registration method for computed tomography (CT) images was proposed and its accuracy and speed were compared with those of other conventional methods. Fifteen 3D CT images obtained from lung biopsy patients were scanned. Each scan consisted of diagnostic and preoperative CT images. Each deformable registration method was initially evaluated with a landmark-based affine registration algorithm. Various deformable registration methods such as level set motion, demons, diffeomorphic demons, and b-spline were compared. Visual assessment by two expert thoracic radiologists using five scales showed an average visual score of 3.2 for level set motion deformable registration, whereas scores were below 3 for other deformable registration methods. In the qualitative assessment, the level set motion algorithm showed better results than those obtained with other deformable registration methods. A level set motion based deformable registration algorithm was effective for registering diagnostic and preoperative volumetric CT images for image-guided lung intervention.