QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
This work addresses the need for fast and accurate neuroanatomy segmentation to support clinical decision-making and large-scale data processing, representing a strong specific gain rather than a foundational advancement.
The authors tackled the problem of slow whole brain segmentation from structural MRI by introducing QuickNAT, a fully convolutional network that segments a brain scan in 20 seconds and achieves superior accuracy and reliability compared to state-of-the-art methods across eight diverse datasets.
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds. To enable training of the complex network with millions of learnable parameters using limited annotated data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are able to use large neuroimaging repositories without manual annotations for training. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior segmentation accuracy and reliability in comparison to state-of-the-art methods, while being orders of magnitude faster. The speed up facilitates processing of large data repositories and supports translation of imaging biomarkers by making them available within seconds for fast clinical decision making.