An Intelligent Detection Method for Pituitary Microadenoma Based on Dynamic Enhanced Magnetic Resonance Images

Abstract: Pituitary microadenomas are usually difficult to detect by non-contrast MRI, and the risk of misdiagnosis is higher and the number of cases is small, which makes the detection, segmentation and classification of pituitary microadenomas difficult. Based on the above problems, a computer-aided diagnostic system DCEPM-CAD based on dynamic enhancement sequence is proposed. While extracting the dynamic enhancement MR sequence timing information, the attention module of HRNetv2 was added to the backbone network to improve. In order to avoid the problem that pituitary microadenomas occupy too few pixels in the image to extract their relevant features, this paper also introduces the TecoGAN image super-resolution method to super-resolution the pituitary region image. In a total of 862 MR image datasets of 275 eligible participants, the diagnostic accuracy of DCEPM-CAD for pituitary microadenomas reached 77%. At the same time, significant results were achieved in the segmentation of pituitary and pituitary microadenomas, and the similarity coefficients of Dice reached 92.16 and 72.54, respectively.