Many medical image segmentation methods have achieved impressive results.Yet,most existing methods do not take into account the shape-intensity prior information.This may lead to implausible segmentation results,in pa...Many medical image segmentation methods have achieved impressive results.Yet,most existing methods do not take into account the shape-intensity prior information.This may lead to implausible segmentation results,in particular for images of unseen datasets.In this paper,we propose a novel approach to incorporate joint shape-intensity prior information into the segmentation network.Specifically,we first train a segmentation network(regarded as the teacher network)on class-wise averaged training images to extract valuable shape-intensity information,which is then transferred to a student segmentation network with the same network architecture as the teacher via knowledge distillation.In this way,the student network regarded as the final segmentation model can effectively integrate the shape-intensity prior information,yielding more accurate segmentation results.Despite its simplicity,experiments on five medical image segmentation tasks of different modalities demonstrate that the proposed Shape-Intensity Knowledge Distillation(SIKD)consistently improves several baseline models(including recent MaxStyle and SAMed)under intra-dataset evaluation,and significantly improves the cross-dataset generalization ability.The source code will be publicly available after acceptance.展开更多
基金supported in part by the National Key Research and Development Program of China(Grant No.2023YFC2705700)the National Natural Science Foundation of China(Grant Nos.62222112,62225113,and 62176186)+1 种基金the Innovative Research Group Project of Hubei Province(Grant No.2024AFA017)the CAAI Huawei MindSpore Open Fund。
文摘Many medical image segmentation methods have achieved impressive results.Yet,most existing methods do not take into account the shape-intensity prior information.This may lead to implausible segmentation results,in particular for images of unseen datasets.In this paper,we propose a novel approach to incorporate joint shape-intensity prior information into the segmentation network.Specifically,we first train a segmentation network(regarded as the teacher network)on class-wise averaged training images to extract valuable shape-intensity information,which is then transferred to a student segmentation network with the same network architecture as the teacher via knowledge distillation.In this way,the student network regarded as the final segmentation model can effectively integrate the shape-intensity prior information,yielding more accurate segmentation results.Despite its simplicity,experiments on five medical image segmentation tasks of different modalities demonstrate that the proposed Shape-Intensity Knowledge Distillation(SIKD)consistently improves several baseline models(including recent MaxStyle and SAMed)under intra-dataset evaluation,and significantly improves the cross-dataset generalization ability.The source code will be publicly available after acceptance.
