Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The purpose of this paper is to present a platform for evaluating segmentation algorithms that detect anatomical structures in medical images. Structure detection being subject to human interpretation,  first describe a method to define a ground truth model, i.e. a generated bronze standard, that will be the reference for subsequent analysis. This bronze standard will be characterized in order to retrieve its confidence level that will later be used to normalize the algorithm evaluation.  then describe how the developed platform helps in evaluating algorithm performances described using five evaluation criteria: accuracy, reliability, robustness, under/over segmentation sensitivity and outlier sensitivity. First,  explain how to extract those evaluation criteria using specific normalized metrics commonly found in the literature, then  present how to combine all the information in order to get a global evaluation of segmentation algorithms. Lastly, a radar-style graph analysis is presented for easy multi-criteria interpretation.
['P. Laurent', 'T. Cresson', 'Carlos Vázquez', 'Nicola Hagemeister', 'Jacques A. de Guise']
I am writing a research paper on a idea A multi-criteria evaluation platform for segmentation algorithms
[{"from": "human", "value": "I am writing a research paper on a idea A multi-criteria evaluation platform for segmentation algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The purpose of this paper is to present a platform for evaluating segmentation algorithms that detect anatomical structures in medical images. Structure detection being subject to human interpretation,  first describe a method to define a ground truth model, i.e. a generated bronze standard, that will be the reference for subsequent analysis. This bronze standard will be characterized in order to retrieve its confidence level that will later be used to normalize the algorithm evaluation.  then describe how the developed platform helps in evaluating algorithm performances described using five evaluation criteria: accuracy, reliability, robustness, under/over segmentation sensitivity and outlier sensitivity. First,  explain how to extract those evaluation criteria using specific normalized metrics commonly found in the literature, then  present how to combine all the information in order to get a global evaluation of segmentation algorithms. Lastly, a radar-style graph analysis is presented for easy multi-criteria interpretation."}]