Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing - Thyreau, Benjamin; Sato, Kazunori; Fukuda, Hiroshi; Taki, Yasuyuki (Medical image analysis, 2018)
•We present a rapid MRI hippocampal segmentation software that has been validated in large cohorts.•The method uses deep learning (3D ConvNets) and learns from other existing (implemented) knowledge.•For training, we used a mixture of real and synthetic data using a powerful augmentation scheme.•The method is generalizable and can be applied to learn features from many medical data problems where labelling is full or approximate. The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model. Deep convolutional neural networks (ConvNets) have shown great success in recent years, due to their ability to learn meaningful features from a mass of training data. Our method relies on the following key novelties: (i) we use a wide and variable training set coming from multiple cohorts (ii) our training labels come in part from the output of the FreeSurfer algorithm, and (iii) we include synthetic data and use a powerful data augmentation scheme. Our method proves to be robust, and it has fast inference (<30s total per subject), with trained model available online (https://github.com/bthyreau/hippodeep). We depict illustrative results and show extensive qualitative and quantitative cohort-wide comparisons with FreeSurfer. Our work demonstrates that deep neural-network methods can easily encode, and even improve, existing anatomical knowledge, even when this knowledge exists in algorithmic form. [Display omitted]