Active shape model is a deformable model which has proven very successful results in the field of image segmentation. The success of ASM model lies in its ability to find the right positions of all landmark points which define the object shape. Intensity profiles are an important part of the Active Shape Models (ASM) which help steer and optimize matching process. However, their simplicity in the standard version of the ASM turns into weakness. The difficulties are met when they are applied to complex structures. The main purpose of this paper is to give a review and discussion about the alternatives proposed in the literature that provide more elaborated intensity models and their impact on the performance of ASM.

The Automatic semantic segmentation of the hippocampus is an important area of research in which several convolutional neural networks (CNN) models have been used to detect the hippocampus from whole cerebral MRI. In this paper we present two convolutional neural networks the first network ( Hippocampus Segmentation Single Entity HSSE) segmented the hippocampus as a single entity and the second used to detect the hippocampal sub-regions ( Hippocampus Segmentation Multi Class HSMC), these two networks inspire their architecture of the U-net model. Two cohorts were used as training data from (NITRC) (NeuroImaging Tools & Resources Collaboratory (NITRC)) annotated by ITK-SNAP software. We analyze this networks alongside other recent methods that do hippocampal segmentation, the results obtained are encouraging and reach dice scores greater than 0.84