Spinal Cord Segmentation in Ultrasound Medical Imagery

Benjdira, Bilel; Ouni, Kaïs; Rahhal, Mohamad Mahmoud Al; Albakr, Abdulrahman; Al‐Habib, Amro; Mahrous, Emad

doi:10.3390/app10041370

Cited by 18 publications

(10 citation statements)

References 29 publications

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“…Multi-scale analysis widens the receptive field, providing context information at various scales. The ASPP module Benjdira et al (2020) is utilized to perform the multi-scale analysis. The input is given to five convolution layers in parallel with 1, 2, 4, 6, and 12 dilation rates, respectively.…”

Section: D Feature Map Classificationmentioning

confidence: 99%

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

Shah

Sinha

2022

Front. Signal Process.

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Electroencephalographic (EEG) signals are electrical signals generated in the brain due to cognitive activities. They are non-invasive and are widely used to assess neurodegenerative conditions, mental load, and sleep patterns. In this work, we explore the utility of representing the inherently single dimensional time-series in different dimensions such as 1D-feature vector, 2D-feature maps, and 3D-videos. The proposed methodology is applied to four diverse datasets: 1) EEG baseline, 2) mental arithmetic, 3) Parkinson’s disease, and 4) emotion dataset. For a 1D analysis, popular 1D features hand-crafted from the time-series are utilized for classification. This performance is compared against the data-driven approach of using raw time-series as the input to the deep learning framework. To assess the efficacy of 2D representation, 2D feature maps that utilize a combination of the Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP) is proposed. This is compared against an approach utilizing a composite feature set consisting of 2D feature maps and 1D features. However, these approaches do not exploit spatial, spectral, and temporal characteristics simultaneously. To address this, 3D EEG videos are created by stacking spectral feature maps obtained from each sub-band per time frame in a temporal domain. The EEG videos are the input to a combination of the Convolution Neural Network (CNN) and Long–Short Term Memory (LSTM) for classification. Performances obtained using the proposed methodologies have surpassed the state-of-the-art for three of the classification scenarios considered in this work, namely, EEG baselines, mental arithmetic, and Parkinson’s disease. The video analysis resulted in 92.5% and 98.81% peak mean accuracies for the EEG baseline and EEG mental arithmetic, respectively. On the other hand, for distinguishing Parkinson’s disease from controls, a peak mean accuracy of 88.51% is achieved using traditional methods on 1D feature vectors. This illustrates that 3D and 2D feature representations are effective for those EEG data where topographical changes in brain activation regions are observed. However, in scenarios where topographical changes are not consistent across subjects of the same class, these methodologies fail. On the other hand, the 1D analysis proves to be significantly effective in the case involving changes in the overall activation of the brain due to varying degrees of deterioration.

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Section: D Feature Map Classificationmentioning

confidence: 99%

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

Shah

Sinha

2022

Front. Signal Process.

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“…This recognition is undertaken through classification among various viruses. We profited from the progress conducted on deep learning [1][2][3][4][5][6][7]. Deep Learning is a sub-field of machine learning dealing with algorithms in tune with the structure and function of the brain-known as artificial neural networks.…”

Section: Covid-19 Diagnosis In Chest X-rays Imagesmentioning

confidence: 99%

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

et al. 2021

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The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.

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“…Baka et al [5] performed data augmentation through mirroring and small free-form deformations in US spine images for bone segmentation. Other works applied random shifting ( [6], [11], [14]), random flipping ( [6], [9], [7], [8], [11], [13], [14]), different translations ( [8], [12]), rotation transformations ( [9], [10], [12], [13], [14]) and varying brightness ( [7], [9]) as an approach for data augmentation. However, these classical augmentations are based on the mechanisms behind optical cameras which strongly differ from the principles of US.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Ultrasound Augmentation: A Physics-Inspired Approach

Tirindelli¹,

Eilers²,

Simson³

et al. 2021

Preprint

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Medical Ultrasound (US), despite its wide use, is characterized by artefacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the training of Deep Neural Networks used for Computer-Assisted Intervention Systems. Data augmentation is commonly used to enhance model generalization and performance. However, common data augmentation techniques, such as affine transformations do not align with the physics of US and, when used carelessly can lead to unrealistic US images. To this end, we propose a set of physics-inspired transformations, including deformation, reverb and Signal-to-Noise Ratio, that we apply on US B-mode images for data augmentation. We evaluate our method on a new spine US dataset for the tasks of bone segmentation and classification.

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Spinal Cord Segmentation in Ultrasound Medical Imagery

Cited by 18 publications

References 29 publications

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

Rethinking Ultrasound Augmentation: A Physics-Inspired Approach

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