CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.

Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to full supervision (median Dice score - VS: 92.5%; Cochleas: 87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.

Fluoride ion accelerating degradation of organic pollutants by Cu(II)-catalyzed Fenton-like reaction at wide pH range.

The degradation of rhodamine B in the F-/Cu(II)/H2O2 system was studied to evaluate the accelerating effect of F- on the Cu(II)-catalyzed Fenton-like reaction. Effects of various parameters, such as F-/Cu2+ ratio, F- and Cu2+ concentrations, initial pH, temperature, H2O2 and rhodamine B concentrations, were investigated. The results confirmed that there is a strong promoting effect of F- on the Cu(II)-catalyzed Fenton-like reaction in a wide pH range of 4-11. In the F-/Cu(II)/H2O2 system the dye could be degraded quickly, and H2O2 had a high utilization. A possible catalytical mechanism was proposed and CuF+ was assumed as the catalytically active species. CuF+ complexed with the OOH- produced by H2O2 dissociation and then rapidly decomposed to Cu(I), which reacted with H2O2 generating hydroxyl radical (HO·), the major reactive oxidizing species.