TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning

Pathological alterations in the human vascular system underlie many chronic diseases, such as atherosclerosis and aneurysms. However, manually analyzing diagnostic images of the vascular system, such as computed tomographic angiograms (CTAs) is a time-consuming and tedious process. To address this issue, we propose a deep learning model to segment the vascular system in CTA images of patients undergoing surgery for peripheral arterial disease (PAD). Our study focused on accurately segmenting the vascular system (1) from the descending thoracic aorta to the iliac bifurcation and (2) from the descending thoracic aorta to the knees in CTA images using deep learning techniques. Our approach achieved average Dice accuracies of 93.5% and 80.64% in test dataset for (1) and (2), respectively, highlighting its high accuracy and potential clinical utility. These findings demonstrate the use of deep learning techniques as a valuable tool for medical professionals to analyze the health of the vascular system efficiently and accurately. Please visit the GitHub page for this paper at https://github.com/pip-alireza/TransOnet.Comment: Accepted for the 2023 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, US

Analysing the cross-section of the abdominal aortic aneurysm neck and its effects on stent deployment

Stent graft devices for the treatment of abdominal aortic aneurysms (AAAs) are being in-creasingly used worldwide. Yet, during modelling and optimization of these devices, as well as in clinical practice, vascular sections are idealized, possibly compromising the effective-ness of the intervention. In this study, we challenge the commonly used approximation of the circular cross-section of the aorta and identify the implications of this approximation to the mechanical assessment of stent grafts. Using computed tomography angiography (CTA) data from 258 AAA patients, the lumen of the aneurysmal neck was analysed. The cross-section of the aortic neck was found to be an independent variable, uncorrelated to other geometrical aspects of the region, and its shape was non-circular reaching elliptical ratios as low as 0.77. These results were used to design a finite element analysis (FEA) study for the assessment of a ring stent bundle deployed under a variety of aortic cross-sections. Re-sults showed that the most common clinical approximations of the vascular cross-section can be a source of significant error when calculating the maximum stent strains (underes-timated by up to 69%) and radial forces (overestimated by up to 13%). Nevertheless, a less frequently used average approximation was shown to yield satisfactory results (5% and 2% of divergence respectively)

Segmentation and Estimation of Brain Tumor Volume in Magnetic Resonance Images Based on T2-Weighted using Hidden Markov Random Field Algorithm

A brain tumor is an abnormal growth of tissue in the brain. The segmentation of brain tumors, which has been manually achieved from magnetic resonance images (MRI) is a decisive and time-consuming task. Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the tumor size, position, and growth pattern. The accuracy and timeliness of detecting a brain tumor are vital factors to achieve the success in diagnosis and treatment of brain tumor. Therefore, segmentation and estimation of volume of brain tumor have been deemed a challenge mission in medical image processing. This paper aims to present a new approach, to improve the segmentation of brain tumors form T2-weighted MRI images using hidden Markov random fields (HMRF) and threshold method. We calculate the volume of the tumor using a new approach based on 2D images measurements and voxel space. The accuracy of segmentation is computed by using the ROC method. In order to validate the proposed approach a comparison is achieved with a manual method using Mango software. This comparison reveals that the noise or impurities in measurement of tumor volume are less in the proposed approach than in Mango softwar

DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans

Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. It is usually asymptomatic and screening recommendation are limited. The gold-standard evaluation is performed by computed tomography angiography (CTA) and radiologists time-consuming assessment. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult, besides increasing the scans quantity for the radiologists. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models reported in the literature. The novel TAA classification model, SAVE-CT, presented accuracies of 0.930 and 0.922 for development and test sets, respectively, using only the binary segmentation mask from DeepVox as input, without hand-engineered features. These two models together are a potential approach for TAA screening, as they can handle variable number of slices as input, handling thoracic and thoracoabdominal sequences, in a fully automated contrast- and dose-independent evaluation. This may assist to decrease TAA mortality and prioritize the evaluation queue of patients for radiologists.Comment: 23 pages, 4 figures, 7 table

High-Pitch, Low-Voltage and Low-Iodine-Concentration CT Angiography of Aorta: Assessment of Image Quality and Radiation Dose with Iterative Reconstruction

Objective: To assess the image quality of aorta obtained by dual-source computed tomography angiography (DSCTA), performed with high pitch, low tube voltage, and low iodine concentration contrast medium (CM) with images reconstructed using iterative reconstruction (IR). Methods: One hundred patients randomly allocated to receive one of two types of CM underwent DSCTA with the electrocardiogram-triggered Flash protocol. In the low-iodine group, 50 patients received CM containing 270 mg I/mL and were scanned at low tube voltage (100 kVp). In the high-iodine CM group, 50 patients received CM containing 370 mg I/mL and were scanned at the tube voltage (120 kVp). The filtered back projection (FBP) algorithm was used for reconstruction in both groups. In addition, the IR algorithm was used in the low-iodine group. Image quality of the aorta was analyzed subjectively by a 3-point grading scale and objectively by measuring the CT attenuation in terms of the signal- and contrast-to-noise ratios (SNR and CNR, respectively). Radiation and CM doses were compared.Results: The CT attenuation, subjective image quality assessment, SNR, and CNR of various aortic regions of interest did not differ significantly between two groups. In the low-iodine group, images reconstructed by FBP and IR demonstrated significant differences in image noise, SNR, and CNR (p<0.05). The low-iodine group resulted in 34.3% less radiation (4.4 ± 0.5 mSv) than the high-iodine group (6.7 ± 0.6 mSv), and 27.3% less iodine weight (20.36 ± 2.65 g) than the high-iodine group (28 ± 1.98 g). Observers exhibited excellent agreement on the aortic image quality scores (κ = 0.904). Conclusions: CT images of aorta could be obtained within 2 s by using a DSCT Flash protocol with low tube voltage, IR, and low-iodine-concentration CM. Appropriate contrast enhancement was achieved while maintaining good image quality and decreasing the radiation and iodine doses

Segmentation of 3D medical images based on region growing method

Táto bakalárska práca sa zaoberá segmentáciou medicínskych objemových dát pomocou metódy narastania oblastí. Cieľom je popísať hlavné metódy 3D segmentácie obrazových dát a zamerať sa najmä na metódu narastania oblastí. Vstupnými dátami sú snímky rezov mozgu z magnetickej rezonancie, ktoré je možné pomocou navrhnutého prehliadača zobrazovať v troch základných rovinách. Prehliadač je realizovaný v programovom prostredí Matlab. Segmentácia obrazových dát je realizovaná metódou semienkového narastania oblastí.This bachalor thesis deals with a region growing approach for segmentation of volumetric medical images. The aim is to present basic methods of segmentation of image data and to focus in particular on the approach of region growing. The input data are brain slices of magnetic resonance imaging which can be visualized using the browser into the three basic planes. The viewer is implemented in MATLAB programming environment. Image segmentation is realized by seeded region growing.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system

Reconstruction and validation of arterial geometries for computational fluid dynamics using multiple temporal frames of 4D flow-MRI magnitude Images

Purpose Segmentation and reconstruction of arterial blood vessels is a fundamental step in the translation of computational fluid dynamics (CFD) to the clinical practice. Four-dimensional flow magnetic resonance imaging (4D Flow-MRI) can provide detailed information of blood flow but processing this information to elucidate the underlying anatomical structures is challenging. In this study, we present a novel approach to create high-contrast anatomical images from retrospective 4D Flow-MRI data. Methods For healthy and clinical cases, the 3D instantaneous velocities at multiple cardiac time steps were superimposed directly onto the 4D Flow-MRI magnitude images and combined into a single composite frame. This new Composite Phase-Contrast Magnetic Resonance Angiogram (CPC-MRA) resulted in enhanced and uniform contrast within the lumen. These images were subsequently segmented and reconstructed to generate 3D arterial models for CFD. Using the time-dependent, 3D incompressible Reynolds-averaged Navier–Stokes equations, the transient aortic haemodynamics was computed within a rigid wall model of patient geometries. Results Validation of these models against the gold standard CT-based approach showed no statistically significant inter-modality difference regarding vessel radius or curvature (p > 0.05), and a similar Dice Similarity Coefficient and Hausdorff Distance. CFD-derived near-wall hemodynamics indicated a significant inter-modality difference (p > 0.05), though these absolute errors were small. When compared to the in vivo data, CFD-derived velocities were qualitatively similar. Conclusion This proof-of-concept study demonstrated that functional 4D Flow-MRI information can be utilized to retrospectively generate anatomical information for CFD models in the absence of standard imaging datasets and intravenous contrast

Automatisierte Segmentierung der Aorta abdominalis in MRT-Daten der NAKO-Studie

Die Auswertung großer medizinischer Bilddatensätze stellt aufgrund der Komplexität und des Umfangs der Daten eine erhebliche Herausforderung dar. Dies gilt im Speziellen auch für das Thema dieser Arbeit: die Vermessung und Formanalyse der Aorta abdominalis (AA) im Rahmen der Nationalen Kohorte (NAKO- Kohortenstudie). Ziel dieser Arbeit war die Implementierung und Evaluation einer Deep Learning (DL)-basierten vollautomatisierten Segmentierung und Formanalyse der AA auf nativen MRT-Daten der NAKO. Aus insgesamt 30.000 MR-Datensätzen wurden n=100 randomisiert für das Training (n=70) und die Testung und Validierung (n=30) des Algorithmus ausgewählt. Durch manuelle Annotation wurden hierauf Trainings- und Validierungsdaten erzeugt. Anschließend konnte durch Einsatz von DL in Form eines Convolutional Neuronal Network (CNN) die vollautomatisierte Segmentierung und Formanalyse der AA durchgeführt werden. Ausgewertet wurden sowohl qualitative als auch quantitative Parameter; die manuelle Segmentierung und Diametermessung diente dabei als Referenz. Die Auswertung der Ergebnisse der automatisierten Segmentierungen und der Diametermessungen erfolgte mittels t-Tests für gepaarte Stichproben und Bland-Altman-Analysen. Die Auswertung ergab gute Ergebnisse bezüglich der automatisierten Segmentierung und Formanalyse. In lediglich einem der 30 Testdatensätzen kam es zu einer signifikanten Fehlsegmentierung. Der mittlere Dice-Score für die automatisierte Segmentierung der Gefäßmasken lag über 0,9 (Maximalwert 1). Die automatisierten Diametermessungen im Rahmen der automatischen Formanalyse zeigten nur geringfügige Abweichungen zur manuellen Referenz. Automatisierte Bildanalysestrategien sind für die Auswertung großer Kohortendatensätze unerlässlich. Wir haben einen vollautomatischen Algorithmus zur robusten Segmentierung und Formanalyse der abdominellen Aorta in nativen MRT-Bildern entwickelt. Auf diese Weise lassen sich reproduzierbare und standardisierte Messungen des vaskulären Phänotyps der abdominellen Aorta durchführen