Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Microfluidic technology has become a valuable tool to the scientific community, allowing researchers to study fine cellular mechanisms with higher variable control compared with conventional systems. It has evolved tremendously, and its applicability and flexibility made its usage grow exponentially and transversely to several research fields. This has been particularly noticeable in neuroscience research, where microfluidic platforms made it possible to address specific questions extending from axonal guidance, synapse formation, or axonal transport to the development of 3D models of the CNS to allow pharmacological testing and drug screening. Furthermore, the continuous upgrade of microfluidic platforms has allowed a deeper study of the communication occurring between different neuronal and glial cells or between neurons and other peripheral tissues, both in physiological and pathological conditions. Importantly, the evolution of microfluidic technology has always been accompanied by the development of new computational tools addressing data acquisition, analysis, and modeling.

Taking a check on chromosome spacing: Animal cells divide by mitosis. Chromosomes become condensed and congregate on the mitotic spindle in the center of the cell—the midzone. The spindle then separates sister chromosomes, pulling them to opposite ends of the cell, ready to form new daughter nuclei. Afonso et al. now show that chromosome separation is monitored by the level of midzone-associated Aurora B kinase activity (see the Perspective by Hadders and Lens). This process ensures that daughter nuclei only reassemble after sister chromosomes have successfully separated. Science , this issue p. 332 ; see also p. 265