Publications by authors named "Quanyou Shen"
Accurate segmentation of prostate anatomy and lesions using biparametric magnetic resonance imaging (bpMRI) is crucial for the diagnosis and treatment of prostate cancer with the aid of artificial intelligence. In prostate bpMRI, different tissues and pathologies are best visualized within specific and narrow ranges for each sequence, which have varying requirements for image window settings. Currently, adjustments to window settings rely on experience, lacking an efficient method for universal automated adjustment.
View Article and Find Full Text PDFArticle Synopsis
- MRI is essential for diagnosing and staging prostate cancer, requiring precise differentiation between the peripheral zone (PZ) and transition zone (TZ) for effective analysis.
- Current segmentation methods face challenges like unclear boundaries and variations in shape and texture, leading to inaccuracies in diagnosis.
- The proposed Enhanced MixFormer and MixUNETR models improve feature extraction from PZ and TZ, enhancing segmentation accuracy in prostate MRI, as shown through extensive experiments on various datasets.