Automated GI tract segmentation using deep learning
원문 초록
The job of Radiation oncologists is to deliver x-ray beams pointed toward the tumor and at the same time avoid the stomach and intestines. With MR-Linacs (magnetic resonance imaging and linear accelerator systems), oncologists can visualize the position of the tumor and allow for precise dose according to tumor cell presence which can vary from day to day. The current job of outlining the position of the stomach and intestines to adjust the X-ray beams direction for the dose delivery to the tumor while avoiding the organs. This is a time-consuming and labor-intensive process that can easily prolong treatments from 15 minutes to an hour a day unless deep learning methods can automate the segmentation process. This paper discusses an automated segmentation process using deep learning to make this process faster and allow more patients to get effective treatment.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
Seg-Wild: Interactive Segmentation based on 3D Gaussian Splatting for Unconstrained Image Collections
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.