Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The 3D segmentation of the left ventricle (LV) in cardiac MRI is a challenging problem, due to the presence of other anatomical structures and artifacts (outliers) around the LV. , a new formulation of a Robust Active Shape Model (RASM) is presented that is able to deal with those outliers. Instead of using the traditional one-to-one mapping of edge points and model points to compute the shape model parameters, the proposed approach uses a one-to-many mapping strategy and groups these edge points into edge segments (strokes). Then, a probabilistic framework provides a robust estimation of the model parameters, in which the influence in the segmentation of the unreliable outliers is reduced. The proposed method was tested on a public dataset comprising 660 volumes. The results indicate that this methodology provides accurate segmentations that are competitive with other state-of-the art methods.
['Carlos Santiago', 'Jacinto C. Nascimento', 'Jorge S. Marques']
I am writing a research paper on a idea A new robust active shape model formulation for cardiac MRI segmentation
[{"from": "human", "value": "I am writing a research paper on a idea A new robust active shape model formulation for cardiac MRI segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The 3D segmentation of the left ventricle (LV) in cardiac MRI is a challenging problem, due to the presence of other anatomical structures and artifacts (outliers) around the LV. , a new formulation of a Robust Active Shape Model (RASM) is presented that is able to deal with those outliers. Instead of using the traditional one-to-one mapping of edge points and model points to compute the shape model parameters, the proposed approach uses a one-to-many mapping strategy and groups these edge points into edge segments (strokes). Then, a probabilistic framework provides a robust estimation of the model parameters, in which the influence in the segmentation of the unreliable outliers is reduced. The proposed method was tested on a public dataset comprising 660 volumes. The results indicate that this methodology provides accurate segmentations that are competitive with other state-of-the art methods."}]