Tag: Medical Imaging

The UW-Madison Musculoskeletal imaging team developed a fully-automated medical imaging diagnostic system using artificial intelligence. This system using a deep learning approach, achieved high diagnostic accuracy and reproducibility in 175 patients with knee pain for detecting cartilage degeneration while reducing subjectivity, variability, and errors associated with human interpretation of knee MR images. This study is now published in Radiology. For more details, please check out the official link or my ResearchGate page.

Illustration of the CNN architecture for the DL-based cartilage lesion detection system. Our proposed method (Left) consisted of segmentation and classification CNNs, which were connected in a cascaded fashion to create a fully-automated processing pipeline. Examples (Right) are shown to demonstrate the successful classification of different types of cartilage lesions.

This April 2018 top-notch medical imaging journal Magnetic Resonance in Medicine (MRM) Editor’s Pick is from Fang Liu and Richard Kijowski, researchers at the University of Wisconsin-Madison. Their paper presents a novel approach to automatically segmenting knee joint structures in magnetic resonance images, combining the power of recently developed deep learning-based artificial intelligence with a highly efficient 3D deformable model approach. The detail for the interview with Fang and Rick about their current and upcoming projects is linked here blog.ismrm.org/2018/04/13/qa-with-and-fang-liu-and-richard-kijowski/