Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An automatic detection method for 197 anatomically defined landmarks in computed tomography (CT) volumes is presented. The proposed method can handle missed landmarks caused by detection failure, a limited imaging range and other problems using a novel combinatorial optimization framework with a two-stage sampling algorithm. After a list of candidates is generated by each landmark detector, the best combination of candidates is searched for by a combinatorial optimization algorithm using a landmark point distribution model (L-PDM) to provide prior knowledge. Optimization is performed by simulated annealing and iterative Gibbs sampling. Prior to each cycle of Gibbs sampling, another sampling algorithm is processed to estimate the spatial distribution of each target landmark, so that landmark positions without any correct detector-derived candidates can be estimated. The proposed method was evaluated using 104 CT volumes with various imaging ranges. The overall average detection distance error was 6.6 mm, and 83.8, 93.2 and 96.5% of landmarks re detected within 10, 15 and 20 mm from the ground truth, respectively. The proposed method worked even when most of the landmarks re outside of the imaging range. The identification accuracy of the vertebral centroid was also evaluated using public datasets and the proposed method could identify 70% of vertebrae including severely diseased ones. From these results, the feasibility of our framework in detecting multiple landmarks in various CT datasets was validated.
['Shouhei Hanaoka', 'Akinobu Shimizu', 'Mitsutaka Nemoto', 'Yukihiro Nomura', 'Soichiro Miki', 'Takeharu Yoshikawa', 'Naoto Hayashi', 'Kuni Ohtomo', 'Yoshitaka Masutani']
I am writing a research paper on a idea Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization
[{"from": "human", "value": "I am writing a research paper on a idea Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An automatic detection method for 197 anatomically defined landmarks in computed tomography (CT) volumes is presented. The proposed method can handle missed landmarks caused by detection failure, a limited imaging range and other problems using a novel combinatorial optimization framework with a two-stage sampling algorithm. After a list of candidates is generated by each landmark detector, the best combination of candidates is searched for by a combinatorial optimization algorithm using a landmark point distribution model (L-PDM) to provide prior knowledge. Optimization is performed by simulated annealing and iterative Gibbs sampling. Prior to each cycle of Gibbs sampling, another sampling algorithm is processed to estimate the spatial distribution of each target landmark, so that landmark positions without any correct detector-derived candidates can be estimated. The proposed method was evaluated using 104 CT volumes with various imaging ranges. The overall average detection distance error was 6.6\u00a0mm, and 83.8, 93.2 and 96.5% of landmarks re detected within 10, 15 and 20\u00a0mm from the ground truth, respectively. The proposed method worked even when most of the landmarks re outside of the imaging range. The identification accuracy of the vertebral centroid was also evaluated using public datasets and the proposed method could identify 70% of vertebrae including severely diseased ones. From these results, the feasibility of our framework in detecting multiple landmarks in various CT datasets was validated."}]