Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.Comment: accepted for MICCAI 201

Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation

Polyrigid and Polyaffine Transformations: A New Class of Diffeomorphisms for Locally Rigid or Affine Registration

MICCAI 2003 Best Student Award in Image Processing and Visualization.International audienceOBJECTIVE: The goal of this work is to improve the usability of a non-rigid registration software for medical images. METHOD: We have built a registration grid service in order to use the interactivity of a visualization workstation and the computing power of a cluster. On the user side, the system is composed of a graphical interface that interacts in a complex and fluid manner with the registration software running on a remote cluster. CONCLUSION: Although the transmission of images back and forth between the computer running the user interface and the cluster running the registration service adds to the total registration time, it provides a user-friendly way of using the registration software without heavy infrastructure investments in hospitals. The system exhibits good performances even if the user is connected to the grid service through a low throughput network such as a wireless network interface or ADSL

Implementation and evaluation of various demons deformable image registration algorithms on GPU

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A grey-scale based DIR algorithm called demons and five of its variants were implemented on GPUs using the Compute Unified Device Architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4DCT images with an average size of 256x256x100 and more than 1,100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 seconds to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency, and ease of implementation.Comment: Submitted to Physics in Medicine and Biolog

An explorative qualitative study to determine the footwear needs of workers in standing environments

Background: Many work places require standing for prolonged periods of time and are potentially damaging to health, with links to musculoskeletal disorders and acute trauma from workplace accidents. Footwear provides the only interaction between the body and the ground and therefore a potential means to impact musculoskeletal disorders. However, there is very limited research into the necessary design and development of footwear based on both the physical environmental constraints and the personal preference of the workers. Therefore, the purpose of this study is to explore workers needs for footwear in the ‘standing’ workplace in relation to MSD, symptoms, comfort and design. Method: Semi-structured interviews were conducted with participants from demanding work environments that require standing for high proportions of the working day. Thematic analysis was used to analyse the results and gain an exploratory understanding into the footwear needs of these workers. Results: Interviews revealed the environmental demands and a very high percentage of musculoskeletal disorders, including day to day discomfort and chronic problems. It was identified that when designing work footwear for standing environments, the functionality of the shoe for the environment must be addressed, the sensations and symptoms of the workers taken into account to encourage adherence and the decision influencers should be met to encourage initial footwear choice. Meeting all these criteria could encourage the use of footwear with the correct safety features and comfort. Development of the correct footwear and increased education regarding foot health and footwear choice could help to reduce or improve the effect of the high number of musculoskeletal disorders repeatedly recorded in jobs that require prolonged periods of standing. Conclusion: This study provides a unique insight into the footwear needs of some workers in environments that require prolonged standing. This user based enquiry has provided information which is important to workplace footwear design

Study protocol for the multicentre cohorts of Zika virus infection in pregnant women, infants, and acute clinical cases in Latin America and the Caribbean: the ZIKAlliance consortium.

BACKGROUND: The European Commission (EC) Horizon 2020 (H2020)-funded ZIKAlliance Consortium designed a multicentre study including pregnant women (PW), children (CH) and natural history (NH) cohorts. Clinical sites were selected over a wide geographic range within Latin America and the Caribbean, taking into account the dynamic course of the ZIKV epidemic. METHODS: Recruitment to the PW cohort will take place in antenatal care clinics. PW will be enrolled regardless of symptoms and followed over the course of pregnancy, approximately every 4 weeks. PW will be revisited at delivery (or after miscarriage/abortion) to assess birth outcomes, including microcephaly and other congenital abnormalities according to the evolving definition of congenital Zika syndrome (CZS). After birth, children will be followed for 2 years in the CH cohort. Follow-up visits are scheduled at ages 1-3, 4-6, 12, and 24 months to assess neurocognitive and developmental milestones. In addition, a NH cohort for the characterization of symptomatic rash/fever illness was designed, including follow-up to capture persisting health problems. Blood, urine, and other biological materials will be collected, and tested for ZIKV and other relevant arboviral diseases (dengue, chikungunya, yellow fever) using RT-PCR or serological methods. A virtual, decentralized biobank will be created. Reciprocal clinical monitoring has been established between partner sites. Substudies of ZIKV seroprevalence, transmission clustering, disabilities and health economics, viral kinetics, the potential role of antibody enhancement, and co-infections will be linked to the cohort studies. DISCUSSION: Results of these large cohort studies will provide better risk estimates for birth defects and other developmental abnormalities associated with ZIKV infection including possible co-factors for the variability of risk estimates between other countries and regions. Additional outcomes include incidence and transmission estimates of ZIKV during and after pregnancy, characterization of short and long-term clinical course following infection and viral kinetics of ZIKV. STUDY REGISTRATIONS: clinicaltrials.gov NCT03188731 (PW cohort), June 15, 2017; clinicaltrials.gov NCT03393286 (CH cohort), January 8, 2018; clinicaltrials.gov NCT03204409 (NH cohort), July 2, 2017