Learning longitudinal classification-regression model for infant hippocampus segmentation

Hippocampus plays an important role in the memory and spatial navigation function of human brain. Study on its growth and change during first year of life would assist the investigation of early brain development as well as the biomarker for neurological disorders. With the help of Magnetic Resonance (MR) imaging techniques, infant brain at different development stage can be acquired with multiple imaging modalities. In this situation, the longitudinal segmentation of infant hippocampus is highly demanded and feasible for the clinical studies regarding to the hippocampal volume changes. However, since the brain structures undergo dynamic appearance, structural changes and various tissue contrast during the first year of life, substantial challenges will be imposed for ensuring the robustness and accuracy of automatic hippocampus segmentation algorithms. In addition, most of the existing hippocampus segmentation methods generally handle each brain development stage independently without considering the potential longitudinal consistency among different stages. In view of the above factors, we propose a longitudinal classification-regression model for segmenting hippocampus in infant brain MRIs. Generally, our model proceeds on a per-timepoint basis, guided by the output of latter timepoint towards the infant hippocampus in the previous timepoint. The key ingredient of our method is a combination of longitudinal context, static context and appearance learning strategies under the classification-regression forest architecture. Specifically, the longitudinal context is borrowed from the mask of prior-timepoint estimation and the static context is from the current-timepoint estimation. Furthermore, we implement the proposed model in a multi-scale and iterative manner to improve the efficiency and effectiveness. The proposed method is evaluated on segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that our method achieves better performance in segmentation accuracy over the state-of-the-art classification and regression random forest model. © 2019 Elsevier B.V.