Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  have been studying the automatic segmentation of multi-organ region from abdominal CT images. In previous work,  proposed an approach using a hierarchical statistical modeling using a relationship beten organs. ,  have proposed automatic segmentation of the upper digestive tract from abdominal contrast-enhanced CT using previously segmented multiple organs.  compared segmentation accuracy of the esophagus, stomach and duodenum beten our proposed method using hierarchical statistical modeling and a conventional statistical atlas method. Additionally, preliminary experiment was performed which added the region representing gas to the candidate region at the segmentation step. The segmentation results re evaluated quantitatively by Dice coefficient, Jaccard index and the average symmetric surface distance of the segmented region and correct region data. Percentage of the average of Dice coefficient of esophagus, stomach and duodenum re 58.7, 68.3, and 38.6 with prediction-based method and 23.7, 51.1, and 24.4 with conventional atlas method. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
['Shunta Hirayama', 'Yoshito Otake', 'Toshiyuki Okada', 'Masatoshi Hori', 'Noriyuki Tomiyama', 'Yoshinobu Sato']
I am writing a research paper on a idea Automated segmentation of upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical modeling of organ interrelations
[{"from": "human", "value": "I am writing a research paper on a idea Automated segmentation of upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical modeling of organ interrelations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  have been studying the automatic segmentation of multi-organ region from abdominal CT images. In previous work,  proposed an approach using a hierarchical statistical modeling using a relationship beten organs. ,  have proposed automatic segmentation of the upper digestive tract from abdominal contrast-enhanced CT using previously segmented multiple organs.  compared segmentation accuracy of the esophagus, stomach and duodenum beten our proposed method using hierarchical statistical modeling and a conventional statistical atlas method. Additionally, preliminary experiment was performed which added the region representing gas to the candidate region at the segmentation step. The segmentation results re evaluated quantitatively by Dice coefficient, Jaccard index and the average symmetric surface distance of the segmented region and correct region data. Percentage of the average of Dice coefficient of esophagus, stomach and duodenum re 58.7, 68.3, and 38.6 with prediction-based method and 23.7, 51.1, and 24.4 with conventional atlas method. \u00a9 (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only."}]