The Continuous Registration Challenge: Evaluation-as-a-Service for Medical Image Registration Algorithms

Marstal, Kasper; Berendsen, Floris F.; Dekker, Niels; Staring, Marius; Klein, Stefan

doi:10.1109/isbi.2019.8759559

Cited by 10 publications

(10 citation statements)

References 14 publications

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“…A detailed analysis of image registration algorithms can be found in the work of Ou et al [2014]. Image registration is also considered as an optimization problem and the optimal optimization technique has to be determined in order to decrease the computational complexity and to increase the accuracy [Penney et al, 1998;Marstal et al, 2019].…”

Section: Preprocessing and Background Adjustmentmentioning

confidence: 99%

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

Uka

Halili

Polisi

et al. 2021

Cells Tissues Organs

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Medical imaging is a growing field that has stemmed from the need to conduct noninvasive diagnosis, monitoring, and analysis of biological systems. With the developments and advances in the medical field and the new techniques that are used in the intervention of diseases, very soon the prevalence of implanted biomedical devices will be even more significant. The implanted materials in a biological system are used in diverse fields, which require lengthy evaluation and validation processes. However, currently the evaluation of the toxicity of biomaterials has not been fully automated yet. Moreover, image analysis is an integral part of biomaterial research, but it is not within the core capacities of a significant portion of biomaterial scientists, which results in the use of predominantly ready-made tools. The detailed image analysis can be conducted once all the relevant parameters including the inherent characteristics of image acquisition techniques are considered. Herein, we cover the currently used image analysis-based techniques for assessment of biomaterial/cell interaction with a specific focus on unstained brightfield microscopy acquired mostly in but not limited to microfluidic systems, which serve as multiparametric sensing platforms for noninvasive experimental measurements. We present the major imaging acquisition techniques that enable point-of-care testing when incorporated with microfluidic cells, discuss the constraints enforced by the geometry of the system and the material that is analyzed, and the challenges that rise in the image analysis when unstained cell imaging is employed. Emerging techniques such as utilization of machine learning and cell-specific pattern recognition algorithms and potential future directions are discussed. Automation and optimization of biomaterial assessment can facilitate the discovery of novel biomaterials together with making the validation of biomedical innovations cheaper and faster.

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Section: Preprocessing and Background Adjustmentmentioning

confidence: 99%

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

Uka

Halili

Polisi

et al. 2021

Cells Tissues Organs

View full text Add to dashboard Cite

show abstract

“…Marstal et al created collaborative platform to registration process, as an open source for the medical algorithms which is the continuous registration challenge (CRC) that involves eight common datasets [79]. Ramamoorthy et al (2019) showed that polycystic ovary syndrome is another women disease made from imbalance hormone of follicle stimulating hormone, and monitoring of cysts grow up by registration technique which apply through these steps; first step, is initial registration which inputs pre-processed US images.…”

Section: Medical Image Registrationmentioning

confidence: 99%

Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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“…ulti-modal medical imaging plays an important role in many clinical applications [1][2][3][4][5][6][7][8][9][10][11], such as image-guided intervention, disease diagnosis, and treatment planning. Among them, multi-contrast magnetic resonance (MR) imaging is one of the most prevalent techniques utilized in brain imaging as different MR imaging sequences can highlight different regions of interest.…”

Section: Introductionmentioning

confidence: 99%

A Coarse-to-Fine Deformable Transformation Framework for Unsupervised Multi-Contrast MR Image Registration with Dual Consistency Constraint

Huang

Yang

Liu

et al. 2021

IEEE Trans. Med. Imaging

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Multi-contrast magnetic resonance (MR) image registration is essential in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to get rid of both the multi-step iteration process and the complex image preprocessing operations. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset that consists of 555 cases, with encouraging performances achieved. Compared to the commonly utilized registration methods, including Voxelmorph, SyN, and LDDMM, the proposed method achieves the best registration performance with a Dice score of 0.826 in identifying stroke lesions. More robust performance in low-signal areas is also observed. With regards to the registration speed, our method is about 17 times faster than the most competitive method of SyN when testing on a same CPU.

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The Continuous Registration Challenge: Evaluation-as-a-Service for Medical Image Registration Algorithms

Cited by 10 publications

References 14 publications

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

Review Research of Medical Image Analysis Using Deep Learning

A Coarse-to-Fine Deformable Transformation Framework for Unsupervised Multi-Contrast MR Image Registration with Dual Consistency Constraint

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