Artificial Intelligence for localization and segmentation of organs in medical imaging

Monday 1 July 2024, 10:30 am
PhD candidate
G.E. Humpire Mamani
prof. dr. B. van Ginneken, prof. dr. W.M. Prokop
dr. ir. C. Jacobs, dr. N. Lessmann

This thesis explores the use of deep learning to enhance the detection and segmentation of structures in CT scans, particularly for oncology patients. Chapter 2 introduces a method for localizing organs in 3D using orthogonal views. Chapter 3 details a CNN-based segmentation algorithm for the spleen, showing high clinical readiness. Chapter 4 addresses the segmentation of kidney abnormalities, highlighting an ablation study to optimize performance. Chapter 5 demonstrates the use of transfer learning to segment additional structures from a partially annotated dataset. The work emphasizes the potential of automated tools to reduce radiologists' workloads, improve accuracy, and aid in clinical decision-making by providing reliable segmentation and localization, which are crucial for diagnosis and treatment planning. The thesis concludes that these methods can significantly contribute to computer-aided detection and diagnosis systems.

Gabriel E Humpire-Mamani graduated from Systems Engineering at the National University of San Agustin, Peru. In 2012, he obtained a master’s degree in computer science at the University of Sao Paulo, Brazil. In February 2016, he started working as a Ph.D. student at DIAG. His doctoral research focused on detecting and segmenting organs in cancer patients.