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Brain organoids: advances, applications and challenges.

ABSTRACT: Brain organoids are self-assembled three-dimensional aggregates generated from pluripotent stem cells with cell types and cytoarchitectures that resemble the embryonic human brain. As such, they have emerged as novel model systems that can be used to investigate human brain development and disorders. Although brain organoids mimic many key features of early human brain development at molecular, cellular, structural and functional levels, some aspects of brain development, such as the formation of distinct cortical neuronal layers, gyrification, and the establishment of complex neuronal circuitry, are not fully recapitulated. Here, we summarize recent advances in the development of brain organoid methodologies and discuss their applications in disease modeling. In addition, we compare current organoid systems to the embryonic human brain, highlighting features that currently can and cannot be recapitulated, and discuss perspectives for advancing current brain organoid technologies to expand their applications.

SUBMITTER: Qian X

PROVIDER: S-EPMC6503989 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Brain organoids: advances, applications and challenges.

Qian Xuyu X Song Hongjun H Ming Guo-Li GL

Development (Cambridge, England) 20190416 8

Brain organoids are self-assembled three-dimensional aggregates generated from pluripotent stem cells with cell types and cytoarchitectures that resemble the embryonic human brain. As such, they have emerged as novel model systems that can be used to investigate human brain development and disorders. Although brain organoids mimic many key features of early human brain development at molecular, cellular, structural and functional levels, some aspects of brain development, such as the formation o ...[more]

PMID: 30992274

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Project description:BackgroundThe limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions.ResultsIn this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths.ConclusionsThe mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community.

Dataset Information

Brain organoids: advances, applications and challenges.

Publications

Brain organoids: advances, applications and challenges.

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