Wyniki wyszukiwania - BazTech

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Whole kidney and renal cortex segmentation in contrast-enhanced MRI using a joint classification and segmentation convolutional neural network

Klepaczko Artur, Majos Marcin, Stefańczyk Ludomir, Eikefjord Eli, Lundervold Arvid

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

295--311

EN

Contrast-enhanced magnetic resonance imaging (CE-MRI) is one of the methods routinely used in clinics for the diagnosis of renal impairments. It allows assessment of kidney perfusion and also visualization of various lesions and tissue atrophy due to e.g. renal artery stenosis (RAS). An important indicator of the renal tissue state is the volume and shape of the kidney. Therefore it is highly desirable to equip radiological units in clinics with the software capable of automatic segmentation of the kidneys in CE-MRI images. This paper proposes a solution to this task using an original architecture of a deep neural network. The proposed design employs a three-branch convolutional neural network specialized in: 1) detection of renal parenchyma within an MR image patch, 2) segmentation of the whole kidney and 3) annotation of the renal cortex. We tested our architecture for normal kidneys in healthy subjects and for poorly perfused organs in RAS patients. The accuracy of renal parenchyma segmentation was equal to 0.94 in terms of the intersection over union (IoU) ratio. Accuracy of the cortex segmentation depends on the level of tissue health condition and ranges from 0.76 up to 0.92 of IoU.

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A systematic review of the automatic kidney segmentation methods in abdominal images

Pandey Mohit, Gupta Abhishek

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1601--1628

EN

Background and Purpose: The precise kidney segmentation is very helpful for diagnosis and treatment planning in urology, by giving information about malformation in the shape and size of the kidney. Kidney segmentation in abdominal computed tomography (CT) images provides support for the efficient and effortless detection of kidney tumors or cancers. Manual kidney segmentation is time-consuming and not reproducible. To overcome this problem, computer-aided automatic approach is used for kidney segmentation. The purpose of presenting this review paper is to analyze different automatic kidney segmentation methods in abdominal CT scans. Materials and Methods: PRISMA guidelines were used to conduct the systematic review. To acquire related articles, three online open source databases were used and a query was formed with relevant keywords. On the basis of inclusion and exclusion criteria, relevant papers were selected from the search results for finding answers to the four evolved research questions. Results: The results reported in the different studies were analyzed based on the formulated research questions. The challenges of these studies were listed to overcome in the future. Many performance parameters representing the results like Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) were compared among the relevant studies. Conclusion: The systematic review article consists of the essence of the several computer-aided kidney segmentation methods using abdominal CT images, which are dedicated to answering the evolved research questions like various methods, accuracy, datasets size, various challenges, and the effect of pathological kidney on the performance of segmentation method had been discussed.

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Automatic Extraction of the Pelvicalyceal System for Preoperative Planning of Minimally Invasive Procedures

Heryan K., Skalski A., Jakubowski J., Drewniak T., Gajda J.

Metrology and Measurement Systems

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2017

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Vol. 24, nr 1

3--18

EN

Minimally invasive procedures for the kidney tumour removal require a 3D visualization of topological relations between kidney, cancer, the pelvicalyceal system and the renal vascular tree. In this paper, a novel methodology of the pelvicalyceal system segmentation is presented. It consists of four following steps: ROI designation, automatic threshold calculation for binarization (approximation of the histogram image data with three exponential functions), automatic extraction of the pelvicalyceal system parts and segmentation by the Locally Adaptive Region Growing algorithm. The proposed method was applied successfully on the Computed Tomography database consisting of 48 kidneys both healthy and cancer affected. The quantitative evaluation (comparison to manual segmentation) and visual assessment proved its effectiveness. The Dice Coefficient of Similarity is equal to 0.871 ± 0.060 and the average Hausdorff distance 0.46 ± 0.36 mm. Additionally, to provide a reliable assessment of the proposed method, it was compared with three other methods. The proposed method is robust regardless of the image acquisition mode, spatial resolution and range of image values. The same framework may be applied to further medical applications beyond preoperative planning for partial nephrectomy enabling to visually assess and to measure the pelvicalyceal system by medical doctors.