2013 | OriginalPaper | Chapter
Incremental Learning with Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy
Authors : Yaozong Gao, Yiqiang Zhan, Dinggang Shen
Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Image-guided radiotherapy (IGRT) requires fast and accurate localization of prostate in treatment CTs, which is challenging due to low tissue contrast and large anatomical variations across patients. On the other hand, in IGRT workflow, a series of CT images is acquired from the same patient under treatment, which contains valuable patient-specific information yet is often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. Particularly, the model is personalized with two steps,
backward pruning
that discards obsolete population-based knowledge, and
forward learning
that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of the specific patient much more accurately. Validated on a large dataset (349 CT scans), our method achieved high localization accuracy (DSC ~0.87) in 4 seconds.