Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

Asymmetric Free-Standing Film with Multifunctional Anti-Bacterial and Self-Cleaning Properties

A superhydrophobic/hydrophilic asymmetric free-standing film has been created using layer-by-layer assembly technique. Poly­(ethylene-imine)-Ag⁺ complex (PEI-Ag⁺) at pH 9.0 was assembled with poly­(acrylic acid) (PAA) at pH 3.2 on a Teflon substrate to yield a micronanostructured surface that can be turned to be superhydrophobic after being coated with a low surface energy compound. Silver nanoparticle loaded free-standing film with one surface being superhydrophobic while the other surface is hydrophilic was then obtained after detachment from the substrate. The superhydrophobicity enabled the upper surface with anti-adhesion and self-cleaning properties and the hydrophilic bottom surface can release silver ions as antibiotic agent. The broad-spectrum antimicrobial capability of silver ions released from the bottom surface coupled with superhydrophobic barrier protection of the upper surface may make the free-standing film a new therapy for open wound

Construction of High Drug Loading and Enzymatic Degradable Multilayer Films for Self-Defense Drug Release and Long-Term Biofilm Inhibition

Bacterial infections and biofilm formation on the surface of implants are important issues that greatly affect biomedical applications and even cause device failure. Construction of high drug loading systems on the surface and control of drug release on-demand is an efficient way to lower the development of resistant bacteria and biofilm formation. In the present study, (montmorillonite/hyaluronic acid-gentamicin)₁₀ ((MMT/HA-GS)₁₀) organic/inorganic hybrid multilayer films were alternately self-assembled on substrates. The loading dosage of GS was as high as 0.85 mg/cm², which could be due the high specific surface area of MMT. The obtained multilayer film with high roughness gradually degraded in hyaluronidase (HAS) solutions or a bacterial infection microenvironment, which caused the responsive release of GS. The release of GS showed dual enzyme and bacterial infection responsiveness, which also indicated good drug retention and on-demand self-defense release properties of the multilayer films. Moreover, the GS release responsiveness to <i>E. coli</i> showed higher sensitivity than that to <i>S. aureus</i>. There was only ∼5 wt % GS release from the film in PBS after 48 h of immersion, and the amount quickly increased to 30 wt % in 10⁵ CFU/mL of <i>E. coli</i>. Importantly, the high drug dosage, smart drug release, and film peeling from the surface contributed to the efficient antibacterial properties and long-term biofilm inhibition functions. Both in vitro and in vivo antibacterial tests indicated efficient sterilization function and good mammalian cell and tissue compatibility

Surface-Adaptive Gold Nanoparticles with Effective Adherence and Enhanced Photothermal Ablation of Methicillin-Resistant Staphylococcus aureus Biofilm

Biofilms that contribute to the persistent bacterial infections pose serious threats to global public health, mainly due to their resistance to antibiotics penetration and escaping innate immune attacks by phagocytes. Here, we report a kind of surface-adaptive gold nanoparticles (AuNPs) exhibiting (1) a self-adaptive target to the acidic microenvironment of biofilm, (2) an enhanced photothermal ablation of methicillin-resistant Staphylococcus aureus (MRSA) biofilm under near-infrared (NIR) light irradiation, and (3) no damage to the healthy tissues around the biofilm. Originally, AuNPs were readily prepared by surface modification with pH-responsive mixed charged zwitterionic self-assembled monolayers consisting of weak electrolytic 11-mercaptoundecanoic acid (HS-C₁₀-COOH) and strong electrolytic (10-mercaptodecyl)­trimethylammonium bromide (HS-C₁₀-N₄). The mixed charged zwitterion-modified AuNPs showed fast pH-responsive transition from negative charge to positive charge, which enabled the AuNPs to disperse well in healthy tissues (pH ∼7.4), while quickly presenting strong adherence to negatively charged bacteria surfaces in MRSA biofilm (pH ∼5.5). Simultaneous AuNP aggregation within the MRSA biofilm enhanced the photothermal ablation of MRSA biofilm under NIR light irradiation. The surrounding healthy tissues showed no damage because the dispersed AuNPs had no photothermal effect under NIR light. In view of the above advantages as well as the straightforward preparation, AuNPs developed in this work may find potential applications as a useful antibacterial agent in the areas of healthcare

Learn2Reg ::comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep Learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org . Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based method