Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lung cancer represents the most deadly type of malignancy. In this work  propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scans in order to provide a radiation therapist with a "second reader" opinion about the tumour location. For each PET slice, our system extracts a set of attributes, passes them to a trained Support Vector Machine (SVM), and returns the optimal threshold value for distinguishing tumour from healthy voxels in that particular slice.  use this technique to analyse four different PET/CT 3D studies. The system produced fairly accurate segmentation, with Jaccard and Dice's similarity coefficients beten 0.82 and 0.98 (the areas outlined by the returned thresholds vs. the ones outlined by the reference thresholds). Besides the high level of geometric similarity, a significant correlation beten the returned and the reference thresholds also indicates that during the training phase, the learning algorithm effectively acquired the dependency beten the extracted attributes and optimal thresholds.
['A. Kerhet', 'C. Small', 'H. Quon', 'Terence Riauka', 'Russell Greiner', 'Alexander McEwan', 'Wilson Roa']
I am writing a research paper on a idea Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach
[{"from": "human", "value": "I am writing a research paper on a idea Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lung cancer represents the most deadly type of malignancy. In this work  propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scans in order to provide a radiation therapist with a \"second reader\" opinion about the tumour location. For each PET slice, our system extracts a set of attributes, passes them to a trained Support Vector Machine (SVM), and returns the optimal threshold value for distinguishing tumour from healthy voxels in that particular slice.  use this technique to analyse four different PET/CT 3D studies. The system produced fairly accurate segmentation, with Jaccard and Dice's similarity coefficients beten 0.82 and 0.98 (the areas outlined by the returned thresholds vs. the ones outlined by the reference thresholds). Besides the high level of geometric similarity, a significant correlation beten the returned and the reference thresholds also indicates that during the training phase, the learning algorithm effectively acquired the dependency beten the extracted attributes and optimal thresholds."}]