The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

In this paper, we aim to better understand the clothing fashion styles. There remain two challenges for us: 1) how to quantitatively describe the fashion styles of various clothing, 2) how to model the subtle relationship between visual features and fashion styles, especially considering the clothing collocations. Using the words that people usually use to describe clothing fashion styles on shopping websites, we build a Fashion Semantic Space (FSS) based on Kobayashi's aesthetics theory to describe clothing fashion styles quantitatively and universally. Then we propose a novel fashion-oriented multimodal deep learning based model, Bimodal Correlative Deep Autoencoder (BCDA) , to capture the internal correlation in clothing collocations. Employing the benchmark dataset we build with 32133 full-body fashion show images, we use BCDA to map the visual features to the FSS. The experiment results indicate that our model outperforms (+13% in terms of MSE) several alternative baselines, confirming that our model can better understand the clothing fashion styles. To further demonstrate the advantages of our model, we conduct some interesting case studies, including fashion trends analyses of brands, clothing collocation recommendation, etc.

Besides fashion, personalization is another important factor of wearing. How to balance fashion trend and personal preference to better appreciate wearing is a non-trivial task. In previous work we develop a demo, Magic Mirror, to recommend clothing collocation based on the fashion trend. However, the diversity of people’s aesthetics is huge. In order to meet different demand, Magic Mirror is upgraded in this paper, and it can give out recommendations by considering both the fashion trend and personal preference, and work as a private clothing consultant. For more suitable recommendation, the virtual consultant will learn users’ tastes and preferences from their behaviors by using Genetic algorithm. Users can get collocations or matched top/bottom recommendation after choosing occasion and style. They can also get a report about their fashion state and aesthetic standpoint on recent wearing.