Retrospective study of deep learning to reduce noise in non-contrast head CT images

Wong, Kelvin; Cummock, Jonathon; He, Yunjie; Ghosh, Rahul; Volpi, John; Wong, Stephen T.C.

doi:10.1016/j.compmedimag.2021.101996

Cited by 12 publications

(10 citation statements)

References 29 publications

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“…These findings are in line with the current literature which shows that DLIR algorithms achieve superior image quality for head CT compared to IR-based algorithms [23][24][25]29]. We confirm the findings from a similar study by Kim et al [23].…”

Section: Discussionsupporting

confidence: 93%

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Deep learning versus iterative image reconstruction algorithm for head CT in trauma

et al. 2022

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Purpose To compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. Methods Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. Results DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. Conclusion The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

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Section: Discussionsupporting

confidence: 93%

“…In a retrospective study, Wong et al [ 29 ] developed a novel DL-based CT image denoising method where the model was trained on noncontrast head CT in patients with acute ischemic stroke. Each CT scan was unique regarding imaging protocol, scanner vendor and model, radiation dose, etc.…”

Section: Discussionmentioning

confidence: 99%

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

et al. 2022

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“…These limitations highlight the importance of computer-aided framework to classify fundus images into different disease diagnosed. Deep learning technology has recently used in various application of life including image classification ( Bukhari et al, 2022 ), text analytics ( Hussain et al, 2022 ; Wahid et al, 2022 ), crisis management ( Sahar et al, 2022 ) and in medical imaging ( Wong et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases

et al. 2023

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Purpose: We aim to present effective and computer aided diagnostics in the field of ophthalmology and improve eye health. This study aims to create an automated deep learning based system for categorizing fundus images into three classes: normal, macular degeneration and tessellated fundus for the timely recognition and treatment of diabetic retinopathy and other diseases.Methods: A total of 1,032 fundus images were collected from 516 patients using fundus camera from Health Management Center, Shenzhen University General Hospital Shenzhen University, Shenzhen 518055, Guangdong, China. Then, Inception V3 and ResNet-50 deep learning models are used to classify fundus images into three classes, Normal, Macular degeneration and tessellated fundus for the timely recognition and treatment of fundus diseases.Results: The experimental results show that the effect of model recognition is the best when the Adam is used as optimizer method, the number of iterations is 150, and 0.00 as the learning rate. According to our proposed approach we, achieved the highest accuracy of 93.81% and 91.76% by using ResNet-50 and Inception V3 after fine-tuned and adjusted hyper parameters according to our classification problem.Conclusion: Our research provides a reference to the clinical diagnosis or screening for diabetic retinopathy and other eye diseases. Our suggested computer aided diagnostics framework will prevent incorrect diagnoses caused by the low image quality and individual experience, and other factors. In future implementations, the ophthalmologists can implement more advanced learning algorithms to improve the accuracy of diagnosis.

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“…Research is conducted using different tools, including DL, a science that has been very popular in recent years in the radiological field. For example, so far in 2021, DL research related to brain cancer can be found, such as Radiation therapy planning of head and neck cancer patients [28], automatic diagnosis of brain tumors [29], detection and classification of brain tumors [30]- [33], diagnostic feasibility assessment with DL networks [34], detection of brain metastases [35], prediction of survival in patients with infiltrating gliomas [36], the prognosis of glioblastoma multiforme [37], analysis for diagnostic biomarkers of glioma [38], segmentation of brain tumors [39]- [42], segmentation in dosimetry in organs at risk [43], and denoising to improve quality in subjective imaging [44].…”

Section: Introductionmentioning

confidence: 99%

“…In general, most authors performed the applications with a small data set; however, they did not implement any data augmentation strategy or learning transfer. Examples of these are: Menze, Al-Saffar, Khairandish, Islam, Song, Poel, Yan, and Wong et al [29], [31], [32], [36]- [38], [43], [44].…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging

Anaya-Isaza

Mera-Jiménez

2022

IEEE Access

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The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and with high performance. In this sense, data augmentation methods are widely used strategies to train networks with small data sets, being vital in medicine due to the limited access to data. A clear example of this is magnetic resonance imaging in pathology scans associated with cancer. In this vein, we compare the effect of several conventional data augmentation schemes on the ResNet50 network for brain tumor detection. In addition, we included our strategy based on principal component analysis. The training was performed with the network trained from zeros and transfer-learning, obtained from the ImageNet dataset. The investigation allowed us to achieve an F1 detection score of 92.34%. The score was achieved with the ResNet50 network through the proposed method and implementing the learning transfer. In addition, it was also concluded that the proposed method is different from the other conventional methods with a significance level of 0.05 through the Kruskal Wallis test statistic.

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Retrospective study of deep learning to reduce noise in non-contrast head CT images

Cited by 12 publications

References 29 publications

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases

Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging

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