Towards Extreme-Resolution Image Registration with Deep Learning

Nazib, Abdullah; Fookes, Clinton; Perrin, Dimitri

doi:10.1109/isbi.2019.8759291

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Deep learning-based algorithms have been applied to the field of medical image processing, such as image registration (4,5), detection (6)(7)(8)(9), segmentation (10)(11)(12), and disease prognosis (13,14). However, to the best of our knowledge, there are limited studies available on the detection of rib fractures on CT images using deep learning methods.…”

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confidence: 99%

Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images

Wu¹,

Chai²,

Qian³

et al. 2021

Radiology: Artificial Intelligence

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To evaluate the performance of a deep learning-based algorithm for automatic detection and labeling of rib fractures from multicenter chest CT images. Materials and Methods: This retrospective study included 10 943 patients (mean age, 55 years; 6418 men) from six hospitals (January 1, 2017 to December 30, 2019), which consisted of patients with and without rib fractures who underwent CT. The patients were separated into one training set (n = 2425), two lesion-level test sets (n = 362 and 105), and one examination-level test set (n = 8051).Free-response receiver operating characteristic (FROC) score (mean sensitivity of seven different false-positive rates), precision, sensitivity, and F1 score were used as metrics to assess rib fracture detection performance. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were employed to evaluate the classification accuracy. The mean Dice coefficient and accuracy were used to assess the performance of rib labeling. Results:In the detection of rib fractures, the model showed an FROC score of 84.3% on test set 1.

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mentioning

confidence: 99%

Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images

Wu¹,

Chai²,

Qian³

et al. 2021

Radiology: Artificial Intelligence

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“…Finally, segmentation-based methods align binary structures (SegReg [ 14 ]). Many of these registration methods—mainly feature- and landmark-based—are supervised algorithms, due to their dependence on manually annotated regions or landmarks [ 15 , 16 , 17 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps

Nicolás-Sáenz

Guerrero-Aspizúa

Pascau

et al. 2020

Entropy

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We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high amount of artifacts due to the complex biopsy preparation, the size of the images, and the complexity of the local morphology. Our method manages to achieve an accurate registration of the tissue cuts and segmentation of the positive antigen areas.

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Towards Extreme-Resolution Image Registration with Deep Learning

Cited by 2 publications

References 16 publications

Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images

Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images

Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps

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