  Longitudinal analysis has great potential to reveal developmental
trajectories and monitor disease progression in medical imaging. This process
relies on consistent and robust joint 4D segmentation. Traditional techniques
are dependent on the similarity of images over time and the use of
subject-specific priors to reduce random variation and improve the robustness
and sensitivity of the overall longitudinal analysis. This is however slow and
computationally intensive as subject-specific templates need to be rebuilt
every time. The focus of this work to accelerate this analysis with the use of
deep learning. The proposed approach is based on deep CNNs and incorporates
semantic segmentation and provides a longitudinal relationship for the same
subject. The proposed approach is based on deep CNNs and incorporates semantic
segmentation and provides a longitudinal relationship for the same subject. The
state of art using 3D patches as inputs to modified Unet provides results
around ${0.91 \pm 0.5}$ Dice and using multi-view atlas in CNNs provide around
the same results. In this work, different models are explored, each offers
better accuracy and fast results while increasing the segmentation quality.
These methods are evaluated on 135 scans from the EADC-ADNI Harmonized
Hippocampus Protocol. Proposed CNN based segmentation approaches demonstrate
how 2D segmentation using prior slices can provide similar results to 3D
segmentation while maintaining good continuity in the 3D dimension and improved
speed. Just using 2D modified sagittal slices provide us a better Dice and
longitudinal analysis for a given subject. For the ADNI dataset, using the
simple UNet CNN technique gives us ${0.84 \pm 0.5}$ and while using modified
CNN techniques on the same input yields ${0.89 \pm 0.5}$. Rate of atrophy and
RMS error are calculated for several test cases using various methods and
analyzed.
