BackgroundAccurate segmentation of gliomas is crucial for diagnosis, treatment planning, and evaluating therapeutic efficacy. Physicians typically analyze and delineate tumor regions in brain magnetic resonance imaging (MRI) images based on personal experience, which is often time‐consuming and subject to individual interpretation. Despite advancements in deep learning technology for image segmentation, current techniques still face challenges in clearly defining tumor boundary contours and enhancing segmentation accuracy.PurposeTo address these issues, this paper proposes a conditional diffusion network (SF‐Diff) with a spatial channel attention infusion (SCAI) module and a frequency spatial attention (FSA) mechanism to achieve accurate segmentation of the whole tumor (WT) region in brain tumors.MethodsSF‐Diff initially extracts multiscale information from multimodal MRI images and subsequently employs a diffusion model to restore boundaries and details, thereby enabling accurate brain tumor segmentation (BraTS). Specifically, a SCAI module is developed to capture multiscale information within and between encoder layers. A dual‐channel upsampling block (DUB) is designed to assist in detail recovery during upsampling. A FSA mechanism is introduced to better match the conditional features with the diffusion probability distribution information. Furthermore, a cross‐model loss function has been implemented to supervise the feature extraction of the conditional model and the noise distribution of the diffusion model.ResultsThe dataset used in this paper is publicly available and includes 369 patient cases from the Multimodal BraTS Challenge 2020 (BraTS2020). The conducted experiments on BraTS2020 demonstrate that SF‐Diff performs better than other state‐of‐the‐art models. The method achieved a Dice score of 91.87%, a Hausdorff 95 of 5.47 mm, an IoU of 84.96%, a sensitivity of 92.29%, and a specificity of 99.95% on BraTS2020.ConclusionsThe proposed SF‐Diff performs well in identifying the WT region of the brain tumors compared to other state‐of‐the‐art models, especially in terms of boundary contours and non‐contiguous lesion regions, which is clinically significant. In the future, we will further develop this method for brain tumor three‐class segmentation task.
