Marginal Space Learning for Medical Image Analysis
Efficient Detection and Segmentation of Anatomical Structures
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Beschreibung
Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications. This book presents an efficient object detection and segmentation framework, called Marginal Space Learning, which runs at a sub-second speed on a current desktop computer, faster than the state-of-the-art. Trained with a sufficient number of data sets, Marginal Space Learning is also robust under imaging artifacts, noise and anatomical variations. The book showcases 35 clinical applications of Marginal Space Learning and its extensions to detecting and segmenting various anatomical structures, such as the heart, liver, lymph nodes and prostate in major medical imaging modalities (CT, MRI, X-Ray and Ultrasound), demonstrating its efficiency and robustness.
Presents an award winning image analysis technology (Thomas Edison Patent Award, MICCAI Young Investigator Award) that achieves object detection and segmentation with state-of-the-art accuracy and efficiency Flexible, machine learning-based framework, applicable across multiple anatomical structures and imaging modalities Thirty five clinical applications on detecting and segmenting anatomical structures such as heart chambers and valves, blood vessels, liver, kidney, prostate, lymph nodes, and sub-cortical brain structures, in CT, MRI, X-Ray and Ultrasound. Includes supplementary material: sn.pub/extras
Autor*in
Yefeng Zheng
Themen in »Marginal Space Learning for Medical Image Analysis«
3D medical image data Anatomical structure detection artificial intelligence computed tomography human body parsing human organ pose estimation intelligent image analysis system machine learning magnetic resonance imaging marginal space learning medical image analysis medical image segmentation medical imaging object detection organ segmentation
Stimmen zu »Marginal Space Learning for Medical Image Analysis«
“This book presents a generic learning-based method for efficient 3D object detection called marginal space learning (MSL). … Each chapter ends with a remarkable bibliography on the topics covered. This book is suited for students and researchers with interest in medical image analysis.” (Oscar Bustos, zbMATH 1362.92004, 2017)
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