Automatic location scheme of anatomical landmarks in 3D head MRI based on the scale attention hourglass network

Li, Sai; Gong, Qiong; Li, Haojiang; Chen, Shuchao; Liu, Yifei; Ruan, Guangying; Zhu, Lin; Liu, Lizhi; Chen, Hongbo

doi:10.1016/j.cmpb.2021.106564

Cited by 4 publications

(1 citation statement)

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“…The emergence of physics‐informed DL methods will allow researchers to develop models that are privy to the underlying physical phenomena, potentially resulting in improved interpretability because the outputs can be evaluated using existing task‐specific knowledge 85–89 . Performing automated and intelligent slice planning for localizers is also an active area of research 90,91 …”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

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Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%