Deep Learning-based Prostate Lesion Segmentation and Classification Using Haralick Texture Maps on MR images

By quantifying pixel relationships from frequencies of local signal intensity spatial variations, Haralick texture features have shown promise for prostate cancer detection. In this study, axial, T2-weighted MR images combined with extracted Haralick texture feature maps were used in a deep learning framework to identify lesion locations and predict Gleason Grade. Results demonstrate potential of Haralick texture features to segment and classify prostate lesions with AUC/sensitivity/specificity of 0.87/0.923/0.776 on patient-level evaluation.