Manual delineation approaches for direct imaging of the subcortex.
Anneke AlkemadeMartijn J MulderAnne C TruttiBirte U ForstmannPublished in: Brain structure & function (2021)
The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of protocols for the anatomical delineation of 21 individual subcortical structures. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients for ten healthy volunteers. In addition, dilated Dice coefficients showed that manual parcellations created using these protocols can provide high-quality training data for automated algorithms. Here, we share the protocols, together with three example MRI datasets and the created manual delineations. The protocols can be applied to create high-quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.
Keyphrases
- magnetic resonance imaging
- machine learning
- deep learning
- high resolution
- contrast enhanced
- white matter
- high throughput
- big data
- endothelial cells
- electronic health record
- computed tomography
- resting state
- healthcare
- mental health
- artificial intelligence
- virtual reality
- photodynamic therapy
- multiple sclerosis
- brain injury
- fluorescence imaging
- silver nanoparticles