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.

Limonin, a Component of Immature Citrus Fruits, Activates Anagen Signaling in Dermal Papilla Cells.

Hair loss remains a significant problem that is difficult to treat; therefore, there is a need to identify safe natural materials that can help patients with hair loss. We evaluated the hair anagen activation effects of limonin, which is abundant in immature citrus fruits. Limonin increased the proliferation of rat dermal papilla cells (rDPC) by changing the levels of cyclin D1 and p27, and increasing the number of BrdU-positive cells. Limonin increased autophagy by decreasing phosphorylated mammalian target of rapamycin levels and increasing the phospho-Raptor, ATG7 and LC3B. Limonin also activated the Wnt/ß-catenin pathway by increasing phospho-ß-catenin levels. XAV939, a Wnt/ß-catenin inhibitor, inhibited these limonin-induced changes, including induced autophagy, BrdU-positive cells, and cell proliferation. Limonin increased the phosphorylated AKT levels in both two-dimensional cultured rDPC and three-dimensional spheroids. Treatment with the PI3K inhibitor wortmannin inhibited limonin-induced proliferation, and disrupted other limonin-mediated changes, including decreased p27, increased BrdU-positive cells, induced autophagy, and increased ATG7 and LC3B levels. Wortmannin also inhibited limonin-induced cyclin D1 and LC3 expression in spheroids. Collectively, these results indicate that limonin can enhance anagen signaling by activating autophagy via targeting the Wnt/ß-catenin and/or PI3K/AKT pathways in rDPC, highlighting a candidate nutrient for hair loss treatment.