Bidirectional elastic image registration using B-spline affine transformation

Gu, Suicheng; Meng, Xin; Sciurba, Frank C.; Ma, Hongwei; Leader, Joseph K.; Kaminski, Naftali; Gur, David; Pu, Jiantao

doi:10.1016/j.compmedimag.2014.01.002

Cited by 22 publications

(6 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a spatial normalization procedure for two section images, which could be acquired at different times or from different locations, the images need to be registered so to find an optimal transformation to align the two images in the same coordinate system. Previously, there have been several algorithms developed for this purpose, particularly in the area of medical image analyses 10 11 12 . In the current study, to improve the calculating efficiency, section image registration and alignment were implemented using the bilinear method 13 .…”

Section: Resultsmentioning

confidence: 99%

Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups

Zhong

Wang

Dong

et al. 2015

Sci Rep

View full text Add to dashboard Cite

Peripheral nerves are important pathways for receiving afferent sensory impulses and sending out efferent motor instructions, as carried out by sensory nerve fibers and motor nerve fibers. It has remained a great challenge to functionally reconnect nerve internal fiber bundles (or fascicles) in nerve repair. One possible solution may be to establish a 3D nerve fascicle visualization system. This study described the key technology of 3D peripheral nerve fascicle reconstruction. Firstly, fixed nerve segments were embedded with position lines, cryostat-sectioned continuously, stained and imaged histologically. Position line cross-sections were identified using a trained support vector machine method, and the coordinates of their central pixels were obtained. Then, nerve section images were registered using the bilinear method, and edges of fascicles were extracted using an improved gradient vector flow snake method. Subsequently, fascicle types were identified automatically using the multi-directional gradient and second-order gradient method. Finally, a 3D virtual model of internal fascicles was obtained after section images were processed. This technique was successfully applied for 3D reconstruction for the median nerve of the hand-wrist and cubital fossa regions and the gastrocnemius nerve. This nerve internal fascicle 3D reconstruction technology would be helpful for aiding peripheral nerve repair and virtual surgery.

show abstract

Section: Resultsmentioning

confidence: 99%

Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups

Zhong

Wang

Dong

et al. 2015

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Chaitanya et al (2019) showed that visually nonrealistic synthetic examples can improve the segmentation of cardiac MRI and noted that it is slightly counter-intuitive-it may have occurred due to the inherent structural and deformationrelated characteristics of the cardiovascular system. Finally, elastic transformations often benefit from B-splines (Huang and Cohen, 1996;Gu et al, 2014) or random deformations (Castro et al, 2018). Diffeomophic mappings play an important role in brain imaging, as they are able to preserve topology and generate biologically plausible deformations.…”

Section: Data Augmentation Using Elastic Image Transformationsmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

261

127

View full text Add to dashboard Cite

Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

show abstract

“…The 120 CT scans in Dataset 1 used to develop the lung and vessel segmentation algorithm had the lung boundaries delineated and other types of lung diseases labeled by an experienced thoracic radiologist (D.P.). Our computational geometric approach [ 19 ] used to identify the intrapulmonary vessels often failed to identify the vessels near the hilum due to the entanglement of the arteries and veins. Therefore, the U-Net framework was used to identify the main extrapulmonary vessels and vessels near the hilum.…”

Section: Methodsmentioning

confidence: 99%

Automated quantification of COVID-19 severity and progression using chest CT images

Leader

Bandos

et al. 2020

Eur Radiol

Self Cite

View full text Add to dashboard Cite

Objective To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. Methods One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. Results There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm 3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression. Conclusion The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. Key Points • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

show abstract

Bidirectional elastic image registration using B-spline affine transformation

Cited by 22 publications

References 52 publications

Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups

Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups

Data Augmentation for Brain-Tumor Segmentation: A Review

Automated quantification of COVID-19 severity and progression using chest CT images

Contact Info

Product

Resources

About