All Outputs (21)

Regional changes in cerebral perfusion with age when accounting for changes in gray-matter volume (2024)
Journal Article
Hu, J., Craig, M. S., Knight, S. P., De Looze, C., Meaney, J. F., Kenney, R. A., Chen, X., & Chappell, M. A. (2024). Regional changes in cerebral perfusion with age when accounting for changes in gray-matter volume. Magnetic Resonance in Medicine, https://doi.org/10.1002/mrm.30376

Purpose
One possible contributing factor for cerebral blood flow (CBF) decline in normal aging is the increase in partial volume effects due to brain atrophy, as cortical thinning can exacerbate the contamination of gray-matter (GM) voxels by other... Read More about Regional changes in cerebral perfusion with age when accounting for changes in gray-matter volume.

Boundary-wise loss for medical image segmentation based on fuzzy rough sets (2024)
Journal Article
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2024). Boundary-wise loss for medical image segmentation based on fuzzy rough sets. Information Sciences, 661, Article 120183. https://doi.org/10.1016/j.ins.2024.120183

The loss function plays an important role in deep learning models as it determines the model convergence behavior and performance. In semantic segmentation, many methods utilize pixel-wise (e.g. cross-entropy) and region-wise (e.g. dice) losses while... Read More about Boundary-wise loss for medical image segmentation based on fuzzy rough sets.

Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation (2023)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., Pekaslan, D., & Garibaldi, J. M. (2023, August). Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Songdo Incheon, Korea

Deep learning models have achieved high performance in numerous semantic segmentation tasks. However, when the input data at test time do not resemble the training data, deep learning models can not handle them properly and will probably produce poor... Read More about Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation.

A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty (2022)
Journal Article
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2022). A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty. IEEE Transactions on Fuzzy Systems, 31(8), 2532-2544. https://doi.org/10.1109/tfuzz.2022.3228332

Deep learning methods have achieved an excellent performance in medical image segmentation. However, the practical application of deep learning-based segmentation models is limited in clinical settings due to the lack of reliable information about th... Read More about A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty.

Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement (2022)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2022, July). Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy

Deep convolutional neural networks (DCNN)-based methods have achieved promising performance in semantic image segmentation. However, in practical applications, it is important not only to produce the segmentation result but also to inform the segment... Read More about Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement.

LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint (2022)
Journal Article
Jafari, M., Francis, S., Garibaldi, J. M., & Chen, X. (2022). LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint. Medical Image Analysis, 81, Article 102536. https://doi.org/10.1016/j.media.2022.102536

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the... Read More about LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint.

Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging—A systematic review (2021)
Journal Article
Jathanna, N., Podlasek, A., Sokol, A., Auer, D., Chen, X., & Jamil-Copley, S. (2021). Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging—A systematic review. Cardiovascular Digital Health Journal, 2(6), S21-S29. https://doi.org/10.1016/j.cvdhj.2021.11.005

Background: Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological chall... Read More about Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging—A systematic review.

FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation (2021)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2021, July). FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation. Presented at IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, Luxembourg

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have c... Read More about FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation.

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management (2020)
Journal Article
Rengasamy, D., Jafari, M., Rothwell, B., Chen, X., & Figueredo, G. P. (2021). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management. Sensors, 20(3), Article 723. https://doi.org/10.3390/s20030723

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architectur... Read More about Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management.

Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation (2019)
Book Chapter
Hou, X., Liu, J., Xu, B., Liu, B., Chen, X., Garibaldi, J., Ilyas, M., Ellis, I., & Qiu, G. (2019). Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II (101-109). Springer Verlag. https://doi.org/10.1007/978-3-030-32245-8_12

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtainin... Read More about Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.

A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets (2019)
Presentation / Conference Contribution
Shen, Z., Chen, X., & Garibaldi, J. M. (2019, June). A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy set... Read More about A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets.

Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework (2019)
Presentation / Conference Contribution
Shen, Z., Chen, X., & Garibaldi, J. (2018, October). Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework. Presented at Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan

© 2018 IEEE. In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector calcul... Read More about Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework.

Neurovascular structures in human vastus lateralis muscle and the ideal biopsy site (2018)
Journal Article
Chen, X., Abbey, S., Bharmal, A., Harris, S., Hudson, E., Krinner, L., Langan, E., Maling, A., Nijran, J., Street, H., Wooley, C., & Billeter, R. (2019). Neurovascular structures in human vastus lateralis muscle and the ideal biopsy site. Scandinavian Journal of Medicine and Science in Sports, 29(4), 504-514. https://doi.org/10.1111/sms.13369

A density model of neurovascular structures was generated from 28 human vastus lateralis muscles isolated from embalmed cadavers. The intramuscular portion of arteries, veins and nerves was dissected, traced on transparencies and digitised before adj... Read More about Neurovascular structures in human vastus lateralis muscle and the ideal biopsy site.

Corneal nerve fractal dimension: a novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy (2018)
Journal Article
Chen, X., Graham, J., Petropoulos, I. N., Ponirakis, G., Asghar, O., & Alam, U. (2018). Corneal nerve fractal dimension: a novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy. Investigative Ophthalmology & Visual Science, 59(2), https://doi.org/10.1167/iovs.17-23342

Objective: Corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, is a noninvasive and objective imaging biomarker for identifying small nerve fiber damage. We have evaluated the diagnostic performance of previously established CC... Read More about Corneal nerve fractal dimension: a novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy.

Efficient deformable motion correction for 3-D abdominal MRI using manifold regression (2017)
Presentation / Conference Contribution
Chen, X., Balfour, D. R., Marsden, P. K., Reader, A. J., Prieto, C., & King, A. P. Efficient deformable motion correction for 3-D abdominal MRI using manifold regression. Presented at International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017)

We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial gold-en-angle trajector... Read More about Efficient deformable motion correction for 3-D abdominal MRI using manifold regression.

High-resolution self-gated dynamic abdominal MRI using manifold alignment (2017)
Journal Article
Chen, X., Usman, M., Baumgartner, C. F., Balfour, D. R., Marsden, P. K., Reader, A. J., Prieto, C., & King, A. P. (2017). High-resolution self-gated dynamic abdominal MRI using manifold alignment. IEEE Transactions on Medical Imaging, 36(4), https://doi.org/10.1109/TMI.2016.2636449

We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free-breathing, high spatial and temporal resolution abdominal MRI sequences. Based on a radial golden-angle (RGA) acquisition traje... Read More about High-resolution self-gated dynamic abdominal MRI using manifold alignment.

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images (2016)
Journal Article
Al-Fahdawi, S., Qahwaji, R., Al-Waisy, A. S., Ipson, S., Malik, R. A., Brahma, A., & Chen, X. (2016). A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images. Computer Methods and Programs in Biomedicine, 135, https://doi.org/10.1016/j.cmpb.2016.07.032

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully aut... Read More about A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images.

An automatic tool for quantification of nerve fibers in corneal confocal microscopy images (2016)
Journal Article
Chen, X., Graham, J., Dabbah, M., Petropoulos, I. N., Tavakoli, M., & Malik, R. (2016). An automatic tool for quantification of nerve fibers in corneal confocal microscopy images. IEEE Transactions on Biomedical Engineering, 64(4), 786-794. https://doi.org/10.1109/TBME.2016.2573642

Objective: We describe and evaluate an automated software tool for nerve-fiber detection and quantification in corneal confocal microscopy (CCM) images, combining sensitive nerve- fiber detection with morphological descriptors. Method: We have evalua... Read More about An automatic tool for quantification of nerve fibers in corneal confocal microscopy images.

Small nerve fibre quantification in the diagnosis of diabetic sensorimotor polyneuropathy: comparing corneal confocal microscopy with intraepidermal nerve fibre density (2015)
Journal Article
Chen, X., Graham, J., Dabbah, M., Petropoulos, I. N., Ponirakis, G., Asghar, O., Alam, U., Marshall, A., Fadavi, H., Ferdousi, M., Azmi, S., Tavakoli, M., Efron, N., Jeziorska, M., & Malik, R. A. (2015). Small nerve fibre quantification in the diagnosis of diabetic sensorimotor polyneuropathy: comparing corneal confocal microscopy with intraepidermal nerve fibre density. Diabetes Care, 38(6), 1138-1144. https://doi.org/10.2337/dc14-2422

OBJECTIVE: Quantitative assessment of small fiber damage is key to the early diagnosis and assessment of progression or regression of diabetic sensorimotor polyneuropathy (DSPN). Intraepidermal nerve fiber density (IENFD) is the current gold standard... Read More about Small nerve fibre quantification in the diagnosis of diabetic sensorimotor polyneuropathy: comparing corneal confocal microscopy with intraepidermal nerve fibre density.

Automatic generation of statistical pose and shape models for articulated joints (2013)
Journal Article
Chen, X., Graham, J., Hutchinson, C., & Muir, L. (2013). Automatic generation of statistical pose and shape models for articulated joints. IEEE Transactions on Medical Imaging, 33(2), https://doi.org/10.1109/TMI.2013.2285503

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint... Read More about Automatic generation of statistical pose and shape models for articulated joints.