An image segmentation algorithm based on multi-scale feature pyramid network
This addresses the need for efficient medical image segmentation in clinical settings, but appears incremental as it builds on existing feature pyramid network methods.
The paper tackles the problem of automatic organ and tumor segmentation in subabdominal MRI images for cervical cancer radiotherapy, aiming to replace time-consuming manual annotation with a fast and accurate algorithm.
Medical image segmentation is particularly critical as a prerequisite for relevant quantitative analysis in the treatment of clinical diseases. For example, in clinical cervical cancer radiotherapy, after acquiring subabdominal MRI images, a fast and accurate image segmentation of organs and tumors in MRI images can optimize the clinical radiotherapy process, whereas traditional approaches use manual annotation by specialist doctors, which is time-consuming and laborious, therefore, automatic organ segmentation of subabdominal MRI images is a valuable research topic.