An Autocontouring Method for the Kidneys Using an Adaptive Weighted Multikernel Support Vector Machines

Abstract

In radiotherapy, the location of the target area is very important. If the target area is small, the treatment is not complete, so the location of the target area is generally larger than the actual cancerous site. However, the damage of radiotherapy to normal cells is the same. In order to reduce the damage to the body as much as possible, we need to complete the most suitable target area. This paper uses an adaptive weighted multikernel support vector machine, which solves the parameter problem in the traditional multikernel support vector machine. The new AW-SVM can adjust the kernel weights adaptively. We completed our experiment on the abdominal MR dataset, using DSI as an evaluation indicator, and the experimental results showed its excellent classification performance. The minimum value of DSI in all results is 0.9654 (more than 0.7 is acceptable).

Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo

“…This correction raises the weighted average Jaccard score from the original 0,31 to 0.85. Previous studies (Zou et al, 2004;Gu and Li, 2021) have reported that for biomedical image segmentation, a DICE score of 0,7 or higher is sufficient. DICE scores can be converted to Jaccard scores using formula J = D/ (2-D), where D is the DICE score, and J is the Jaccard score.…”