Saskia Camps

About

Saskia Camps is a leading researcher at the intersection of deep learning, medical imaging, and robotic surgery, with a primary focus on advancing autonomous knee arthroscopy and image-guided radiotherapy. Her most impactful contribution is the development of a deep learning-based framework for automatic femoral cartilage segmentation in ultrasound imaging, a critical innovation for guiding robotic systems during minimally invasive knee surgery. This work, published in 2019 and cited 47 times, directly addresses the risk of unintended cartilage injury and postoperative complications by enabling real-time, volumetric ultrasound guidance without the need for a skilled operator. Camps further advanced this field by creating a deep learning model for automatic ultrasound image quality assessment, ensuring that only diagnostically useful images are used for surgical navigation. Her earlier work on automatic transperineal ultrasound probe positioning for radiotherapy, though less cited, laid the groundwork for reducing operator dependency in image acquisition—a key barrier to wider ultrasound adoption. Through these contributions, Camps is helping to make autonomous robotic surgery safer and more reliable, with her research forming a foundational step toward fully automated, ultrasound-guided orthopedic procedures.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy

   47 citations · 2019
2. 2
   Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy

   15 citations · 2020
3. 3
   Automatic transperineal ultrasound probe positioning based on CT scan for image guided radiotherapy

   4 citations · 2017

Key Collaborators

Contact & Links