| 1. |
- Ali, Hazrat, et al.
(författare)
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A Deep Learning Pipeline for Identification of Motor Units in Musculoskeletal Ultrasound
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 170595-170608
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Tidskriftsartikel (refereegranskat)abstract
- Skeletal muscles are functionally regulated by populations of so-called motor units (MUs). An MU comprises a bundle of muscle fibers controlled by a neuron from the spinal cord. Current methods to diagnose neuromuscular diseases and monitor rehabilitation, and study sports sciences rely on recording and analyzing the bio-electric activity of the MUs. However, these methods provide information from a limited part of a muscle. Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train). We train and evaluate the model using simulated data mimicking the complex activation pattern of tens of activated MUs with overlapping territories and partially synchronized activation patterns. Using a slow fusion approach (based on 3D CNNs), we transform the spatiotemporal image sequence data to 2D representations and apply a deep neural network architecture for segmentation. Next, we employ a second deep neural network architecture for signal estimation. The results show that the proposed pipeline can effectively identify individual MUs, estimate their territories, and estimate their twitch train signal at low contraction forces. The framework can retain spatio-temporal consistencies and information of the mechanical response of MU activity even when the ultrasound image sequences are transformed into a 2D representation for compatibility with more traditional computer vision and image processing techniques. The proposed pipeline is potentially useful to identify simultaneously active MUs in whole muscles in ultrasound image sequences of voluntary skeletal muscle contractions at low force levels.
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| 2. |
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| 3. |
- Ali, Hazrat, et al.
(författare)
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Leveraging GANs for data scarcity of COVID-19 : Beyond the hype
- 2023
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Ingår i: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). - : IEEE Computer Society. - 9798350302493 ; , s. 659-667
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Konferensbidrag (refereegranskat)abstract
- Artificial Intelligence (AI)-based models can help in diagnosing COVID-19 from lung CT scans and X-ray images; however, these models require large amounts of data for training and validation. Many researchers studied Generative Adversarial Networks (GANs) for producing synthetic lung CT scans and X-Ray images to improve the performance of AI-based models. It is not well explored how good GAN-based methods performed to generate reliable synthetic data. This work analyzes 43 published studies that reported GANs for synthetic data generation. Many of these studies suffered data bias, lack of reproducibility, and lack of feedback from the radiologists or other domain experts. A common issue in these studies is the unavailability of the source code, hindering reproducibility. The included studies reported rescaling of the input images to train the existing GANs architecture without providing clinical insights on how the rescaling was motivated. Finally, even though GAN-based methods have the potential for data augmentation and improving the training of AI-based models, these methods fall short in terms of their use in clinical practice. This paper highlights research hotspots in countering the data scarcity problem, identifies various issues as well as potentials, and provides recommendations to guide future research. These recommendations might be useful to improve acceptability for the GAN-based approaches for data augmentation as GANs for data augmentation are increasingly becoming popular in the AI and medical imaging research community.
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| 4. |
- Ali, Hazrat, et al.
(författare)
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Modelling intra-muscular contraction dynamics using in silico to in vivo domain translation
- 2022
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Ingår i: Biomedical engineering online. - : BioMed Central (BMC). - 1475-925X. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Advances in sports medicine, rehabilitation applications and diagnostics of neuromuscular disorders are based on the analysis of skeletal muscle contractions. Recently, medical imaging techniques have transformed the study of muscle contractions, by allowing identifcation of individual motor units’ activity, within the whole studied muscle. However, appropriate image-based simulation models, which would assist the continued development of these new imaging methods are missing. This is mainly due to a lack of models that describe the complex interaction between tissues within a muscle and its surroundings, e.g., muscle fbres, fascia, vasculature, bone, skin, and subcutaneous fat. Herein, we propose a new approach to overcome this limitation.Methods: In this work, we propose to use deep learning to model the authentic intramuscular skeletal muscle contraction pattern using domain-to-domain translation between in silico (simulated) and in vivo (experimental) image sequences of skeletal muscle contraction dynamics. For this purpose, the 3D cycle generative adversarial network (cycleGAN) models were evaluated on several hyperparameter settings and modifcations. The results show that there were large diferences between the spatial features of in silico and in vivo data, and that a model could be trained to generate authentic spatio-temporal features similar to those obtained from in vivo experimental data. In addition, we used diference maps between input and output of the trained model generator to study the translated characteristics of in vivo data.Results: This work provides a model to generate authentic intra-muscular skeletal muscle contraction dynamics that could be used to gain further and much needed physiological and pathological insights and assess and overcome limitations within the newly developed research feld of neuromuscular imaging.
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| 5. |
- Ali, Hazrat, et al.
(författare)
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Translation of atherosclerotic disease features onto healthy carotid ultrasound images using domain-to-domain translation
- 2023
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Ingår i: Biomedical Signal Processing and Control. - 1746-8094 .- 1746-8108. ; 85
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Tidskriftsartikel (refereegranskat)abstract
- Objective: In this work, we evaluated a model for the translation of atherosclerotic disease features onto healthy carotid ultrasound images.Methods: An un-paired domain-to-domain translation model – the cycle Generative Adversarial Network (cycleGAN) – was trained to translate between carotid ultrasound images of healthy arteries and images of pronounced disease. Translation performance was evaluated using the measurement of wall thickness in original and generated images. In addition, we explored disease translation in different tissue segments (subcutaneous tissue, muscle, lumen, far wall, and deep tissues), using structural similarity index measure (SSIM) maps.Results: Features of pronounced disease were successfully translated to the healthy images (1.2 (0.33) mm vs 0.43 (0.07) mm, p < 0.001), while overall anatomy was retained as SSIM value was equal to 0.78 (0.02). Exploration of translated features showed that both arterial wall and subcutaneous tissues were modified in the translation, but that the subcutaneous tissue was subject to distortion of the anatomy in some cases. The image quality influenced the disease translation performance.Conclusion: The results show that the model can learn a mapping between healthy and diseased images while retaining the overall anatomical contents. This is the first study on atherosclerosis disease translation in medical images.Significance: The concept of translating disease onto existing healthy images may serve purposes such as education, cardiovascular risk communication in health conversations, or personalized modelling in precision medicine.
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| 6. |
- Jiang, Biao, et al.
(författare)
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Imaging carotid wall mechanical heterogeneity in ultrasound image sequences using Eulerian video magnification
- 2020
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Ingår i: Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. - : IEEE. - 9781728195742 ; , s. 785-788
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Konferensbidrag (refereegranskat)abstract
- Atherosclerosis is a cardiovascular disease causing gradual infiltration of fatty streaks in the arterial wall and eventually plaque build-up that may rupture and cause a stroke. This induces changes in local biomechanics in response to systemic pressure variations. Such mechanical heterogeneities in the carotid arteries can be studied using ultrasound imaging methods. However, in 2-D ultrasound imaging, mechanical heterogeneity may result in both in-plane motions as well as out-of-plane motion, the latter causing intensity variations. Here, we evaluate linear Eulerian video magnification (EVM) processing on carotid ultrasound image sequences, and its ability to image local mechanical heterogeneity. In addition, we explore the method on several ultrasound image sequences from carotid wall tissues at different atherosclerotic disease stages ranging from healthy, early and late atherosclerosis, as well as changes with pharmacological treatment. The results show that linear EVM can be used to magnify motions in carotid ultrasound image sequences, and to derive heterogeneity maps that can visualize mechanical aspects of the carotid walls and its composition. Empirical case study on carotid walls indicate that the heterogeneity maps transition from homogenic to heterogenic pattern with progression of the atherosclerotic disease. The findings of this work show that mechanical heterogeneity imaging may be important in assessing atherosclerotic disease progression and potential risk prediction.
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| 7. |
- Mubashar, Mehreen, et al.
(författare)
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R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation
- 2022
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Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 34:20, s. 17723-17739
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Tidskriftsartikel (refereegranskat)abstract
- U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.
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| 8. |
- Munawar, Faizan, et al.
(författare)
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Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
- 2020
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8, s. 153535-153545
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Tidskriftsartikel (refereegranskat)abstract
- Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results.
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| 9. |
- Viggiano, Annarita, et al.
(författare)
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Pathway for the biosynthesis of the pigment chrysogine by Penicillium chrysogenum
- 2018
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Ingår i: Applied and Environmental Microbiology. - 1098-5336 .- 0099-2240. ; 84:4
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Tidskriftsartikel (refereegranskat)abstract
- Chrysogine is a yellow pigment produced by Penicillium chrysogenum and other filamentous fungi. Although the pigment was first isolated in 1973, its biosynthetic pathway has so far not been resolved. Here, we show that deletion of the highly expressed nonribosomal peptide synthetase (NRPS) gene Pc21g12630 (chyA) resulted in a decrease in the production of chrysogine and 13 related compounds in the culture broth of P. chrysogenum. Each of the genes of the chyAcontaining gene cluster was individually deleted, and corresponding mutants were examined by metabolic profiling in order to elucidate their function. The data suggest that the NRPS ChyA mediates the condensation of anthranilic acid and alanine into the intermediate 2-(2-aminopropanamido)benzoic acid, which was verified by feeding experiments of a ΔchyA strain with the chemically synthesized product. The remainder of the pathway is highly branched, yielding at least 13 chrysogine-related compounds.
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