| 1. |
- Khodadad, Davood, 1985-, et al.
(författare)
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Optimized breath detection algorithm in electrical impedance tomography
- 2018
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Ingår i: Physiological Measurement. - : IOP Publishing. - 0967-3334 .- 1361-6579. ; 39:9
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Tidskriftsartikel (refereegranskat)abstract
- Objective: This paper defines a method for optimizing the breath delineation algorithms used in electrical impedance tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern.Approach: Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project.Main results: Three different algorithms are proposed that improve the breath detector performance by adding conditions on (1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, (2) minimum tidal impedance amplitude and (3) minimum tidal breath rate obtained from time-frequency analysis. As a baseline a zero-crossing algorithm has been used with some default parameters based on the Swisstom EIT device.Significance: Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the receiver operating characterics. This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.
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| 2. |
- Y Banaem, Hossein, et al.
(författare)
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Brain tumor modeling : glioma growth and interaction with chemotherapy
- 2011
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Ingår i: International Conference on Graphic and Image Processing (ICGIP 2011). - : SPIE. ; 8285
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Konferensbidrag (refereegranskat)abstract
- In last decade increasingly mathematical models of tumor growths have been studied, particularly on solid tumors which growth mainly caused by cellular proliferation. In this paper we propose a modified model to simulate the growth of gliomas in different stages. Glioma growth is modeled by a reaction-advection-diffusion. We begin with a model of untreated gliomas and continue with models of polyclonal glioma following chemotherapy. From relatively simple assumptions involving homogeneous brain tissue bounded by a few gross anatomical landmarks (ventricles and skull) the models have been expanded to include heterogeneous brain tissue with different motilities of glioma cells in grey and white matter. Tumor growth is characterized by a dangerous change in the control mechanisms, which normally maintain a balance between the rate of proliferation and the rate of apoptosis (controlled cell death). Result shows that this model closes to clinical finding and can simulate brain tumor behavior properly.
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| 3. |
- Yousefi, Hossein, et al.
(författare)
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An optimised linear mechanical model for estimating brain shift caused by meningioma tumours
- 2013
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Ingår i: International Journal of Biomedical Science and Engineering. - : Science Publishing Group. - 2376-7227 .- 2376-7235. ; 1:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of brain deformation plays an important role in computer-aided therapy and image-guided neurosurgery systems. Tumour growth can cause brain deformation and change stress distribution in the brain. Biomechanical models exist that use a finite element method to estimate brain shift caused by tumour growth. Such models can be categorised as linear and non-linear models, both of which assume finite deformation of the brain after tumour growth. Linear models are easy to implement and fast enough to for applications such as IGS where the time is a great of concern. However their accuracy highly dependent on the parameters of the models in this paper, we proposed an optimisation approach to improve a naive linear model to achieve more precise estimation of brain displacements caused by tumour growth. The optimisation process has improved the accuracy of the model by adapting the brain model parameters according to different tomour sizes.We used patient-based tetrahedron finite element mesh with proper material properties for brain tissue and appropriate boundary conditions in the tumour region. Anatomical landmarks were determined by an expert and were divided into two different sets for evaluation and optimisation. Tetrahedral finite element meshes were used and the model parameters were optimised by minimising the mean square distance between the predicted locations of the anatomical landmarks derived from Brain Atlas images and their actual locations on the tumour images. Our results demonstrate great improvement in the accuracy of an optimised linear mechanical model that achieved an accuracy rate of approximately 92%.
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| 4. |
- Khodadad, Davood, 1985-, et al.
(författare)
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The Value of Phase Angle in Electrical Impedance Tomography Breath Detection
- 2018
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Ingår i: 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama). - : Electromagnetics Academy. - 9784885523168 - 9781538654552 ; , s. 1040-1043
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Konferensbidrag (refereegranskat)abstract
- The objective of this paper is to report our investigation demonstrating that the phase angle information of complex impedance could be a simple indicator of a breath cycle in chest Electrical Impedance Tomography (EIT). The study used clinical neonatal EIT data. The results show that measurement of the phase angle from complex EIT data can be used as a complementary information for improving the conventional breath detection algorithms.
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| 5. |
- Nordebo, Sven, 1963-, et al.
(författare)
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A parametric model for the changes in the complex valued conductivity of a lung during tidal breathing
- 2018
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Ingår i: Journal of Physics D. - : IOP Publishing. - 0022-3727 .- 1361-6463. ; 51:20
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Tidskriftsartikel (refereegranskat)abstract
- Classical homogenization theory based on the Hashin-Shtrikman coated ellipsoids is used to model the changes in the complex valued conductivity (or admittivity) of a lung during tidal breathing. Here, the lung is modeled as a two-phase composite material where the alveolar air-filling corresponds to the inclusion phase. The theory predicts a linear relationship between the real and the imaginary parts of the change in the complex valued conductivity of a lung during tidal breathing, and where the loss cotangent of the change is approximately the same as of the effective background conductivity and hence easy to estimate. The theory is illustrated with numerical examples based on realistic parameter values and frequency ranges used with electrical impedance tomography (EIT). The theory may be potentially useful for imaging and clinical evaluations in connection with lung EIT for respiratory management and control.
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| 6. |
- Ahmed, Ammar, et al.
(författare)
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Learning from the few : Fine-grained approach to pediatric wrist pathology recognition on a limited dataset
- 2024
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 181
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Tidskriftsartikel (refereegranskat)abstract
- Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. While X-ray imaging remains a prevalent diagnostic tool, the increasing misinterpretation rates highlight the need for more accurate analysis, especially considering the lack of specialized training among many surgeons and physicians. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. However, distinguishing subtle variations between pediatric wrist pathologies in X-rays remains challenging. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. In this paper, we address the challenge of pediatric wrist pathology recognition with a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention. We refine our fine-grained architecture through ablation analysis and the integration of LION. Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition.
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| 7. |
- Akhlaq, Filza, et al.
(författare)
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Diving Deep into Bone Anomalies on the FracAtlas Dataset Using Deep Learning and Explainable AI
- 2024
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Ingår i: Proceedings of the 2024 International Conference on Engineering & Computing Technologies (ICECT). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- Medical image analysis has undergone significant advancements with the integration of machine learning techniques, particularly in the realm of bone anomaly detection. The availability of recent datasets and the lack of benchmarking and explainability components provide numerous opportunities in this domain. This study proposes a benchmarking approach to a recently published FracAtlas dataset utilizing state-of-the-art deep-learning models coupled with explainable artificial intelligence (XAI) having two distinct modules. The first module involves the binary classification of fractures in different body parts and explains the decision-making process of the best-performing model using an XAI technique known as EigenCAM. EigenCAM generates heatmaps on every layer of the YOLOv8m model to explain how the model reached a conclusion and localizes the fracture using a heatmap. To verify the heatmap, we also detected fractures using the YOLOv8m detection model, which achieved a mAP@O.5 of 59.5%, outperforming the baseline results on this dataset. The second module involves a multi-class classification task to categorize images into one of the five anatomical regions. The best-performing model for binary classification is the YOLOv8m model, with an accuracy of 83.1%, whereas the best-performing model for multi-class classification is the YOLOv8s, achieving an accuracy of 96.2%.
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| 8. |
- Becher, Tobias H., et al.
(författare)
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Prolonged Continuous Monitoring of Regional Lung Function in Infants with Respiratory Failure
- 2022
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Ingår i: Annals of the American Thoracic Society. - : American Thoracic Society. - 2329-6933 .- 2325-6621. ; 19:6, s. 991-999
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Tidskriftsartikel (refereegranskat)abstract
- Rationale: Electrical impedance tomography (EIT) allows instantaneous and continuous visualization of regional ventilation and changes in end-expiratory lung volume at the bedside. There is particular interest in using EIT for monitoring in critically ill neonates and young children with respiratory failure. Previous studies have focused only on short-term monitoring in small populations. The feasibility and safety of prolonged monitoring with EIT in neonates and young children have not been demonstrated yet.Objectives: To evaluate the feasibility and safety of long-term EIT monitoring in a routine clinical setting and to describe changes in ventilation distribution and homogeneity over time and with positioning in a multicenter cohort of neonates and young children with respiratory failure.Methods: At four European University hospitals, we conducted an observational study (NCT02962505) on 200 patients with postmenstrual ages (PMA) between 25 weeks and 36 months, at risk for or suffering from respiratory failure. Continuous EIT data were obtained using a novel textile 32-electrode interface and recorded at 48 images/s for up to 72 hours. Clinicians were blinded to EIT images during the recording. EIT parameters and the effects of body position on ventilation distribution were analyzed offline. Results: The average duration of FAT measurements was 53 +/- 20 hours. Skin contact impedance was sufficient to allow image reconstruction for valid ventilation analysis during a median of 92% (interquartile range, 77-98%) of examination time. EIT examinations were well tolerated, with minor skin irritations (temporary redness or imprint) occurring in 10% of patients and no moderate or severe adverse events. Higher ventilation amplitude was found in the dorsal and right lung areas when compared with the ventral and left regions, respectively. Prone positioning resulted in an increase in the ventilation-related EIT signal in the dorsal hemithorax, indicating increased ventilation of the dorsal lung areas. Lateral positioning led to a redistribution of ventilation toward the dependent lung in preterm infants and to the nondependent lung in patients with PMA > 37 weeks.Conclusions: EIT allows continuous long-term monitoring of regional lung function in neonates and young children for up to 72 hours with minimal adverse effects. Our study confirmed the presence of posture-dependent changes in ventilation distribution and their dependency on PMA in a large patient cohort.
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| 9. |
- Bergström, Per, et al.
(författare)
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Single shot shape evaluation using dual-wavelength holographic reconstructions and regularization
- 2014
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Ingår i: Fringe 2013. - Berlin : Encyclopedia of Global Archaeology/Springer Verlag. - 9783642363580 - 9783642363597 ; , s. 103-108
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Konferensbidrag (refereegranskat)abstract
- The aim of this work is to evaluate the shape of a free form object using single shot digital holography. The digital holography results in a gradient field and wrapped phase maps representing the shape of the object. The task is then to find a surface representation from this data which is an inverse problem. To solve this inverse problem we are using regularization with additional shape information from the CAD-model of the measured object.
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| 10. |
- Jasmine Pemeena Priyadarsini, M., et al.
(författare)
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Lung Diseases Detection Using Various Deep Learning Algorithms
- 2023
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Ingår i: Journal of Healthcare Engineering. - : Hindawi Publishing Corporation. - 2040-2295 .- 2040-2309. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
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