Research On Organ Quality Evaluation Algorithm In CT Image Based On Multi-Model Joint Feedback

As a multiple malignant disease,cancer is highly lethal and difficult to eradicate surgically.As a comprehensive treatment method,radiotherapy is the most commonly used method for treating cancer.When doctors make radiotherapy plans,they should pay attention to the delineation of organs that are dangerous to avoid radiation damage to healthy organs.In order to reduce the workload of radiologists,accurate automatic segmentation of endangered organs has become an urgent task.However,the segmentation of endangered organs is easily affected by factors such as imbalance between organ volumes and metal artifacts,and interference factors only exist in local areas of the CT image.Therefore,using a single segmentation model to segment all the organs at risk in the entire CT image can lead to a large gap in segmentation results.Firstly,considering that existing segmentation algorithms focus solely on the entire image and do not consider the differences between local information in the image,this paper proposes a quality evaluation method based on the difficulty of segmentation of a single threatened organ in a CT image,which aims to provide a referential quantitative indicator for subsequent automatic delineation of threatened organs,Based on this indicator,it is possible to select an appropriate segmentation model for each organ during the organ segmentation process,and it can also remind doctors to accurately modify and delineate more challenging organs when correcting labeled data.Secondly,in order to predict the quality score representing the difficulty of segmentation of a single organ at risk in a CT image,this paper proposes a feedback scoring mechanism that combines multiple segmentation models.Based on the strong correlation between the segmentation results of different segmentation models,a quality score label for a single organ is generated.Moreover,in order to achieve automatic organ quality evaluation,this paper extracts relevant features of the organ regions in the image,trains and predicts the organ quality score.Finally,in order to verify the effectiveness of the proposed method in this article,experimental validation was conducted on the head and neck dataset and the abdominal dataset,respectively.Firstly,the quality evaluation algorithm is validated,and the features within the organ region are extracted for regression training and prediction.The experimental results show that the proposed quality evaluation algorithm can automatically and accurately predict the difficulty of organ segmentation,and the predicted results maintain a strong correlation with the true values.In order to further validate the effectiveness of the proposed algorithm,this paper compares the segmentation results with those of traditional segmentation models without quality evaluation.The experimental results show that for the nine types of small organs in the head and neck,the overall Dice coefficient increases by 2.16% and the False Negative Rate decreases by3.85%;For the three major organs of the abdomen,although the overall Dice coefficient only increased by 0.34%,there was a significant decrease in the False Negative Rate,which decreased by 3.14%.Therefore,it can be proven that quality evaluation has an improvement effect on segmentation performance.