Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter,  propose two new fusion methods: “Global ighted Shape-Based Averaging” (GWSBA) and “Local ighted Shape-Based Averaging” (LWSBA). These methods extend the ll known Shape-Based Averaging (SBA) by additionally incorporating the similarity information beten the reference (i.e., atlas) images and the target image to be segmented.  also propose a new spatially-varying similarity-ighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods.  first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.
['Sai Subrahmanyam Gorthi', 'Meritxell Bach Cuadra', 'Pierre-Alain Tercier', 'Abdelkarim S. Allal', 'Jean-Philippe Thiran']
I am writing a research paper on a idea Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter,  propose two new fusion methods: \u201cGlobal ighted Shape-Based Averaging\u201d (GWSBA) and \u201cLocal ighted Shape-Based Averaging\u201d (LWSBA). These methods extend the ll known Shape-Based Averaging (SBA) by additionally incorporating the similarity information beten the reference (i.e., atlas) images and the target image to be segmented.  also propose a new spatially-varying similarity-ighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods.  first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA."}]