Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018.

The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections:
Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods.
Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications.
Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods.
Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. 

• Artificial intelligence
• Classification
• Computer vision
• Estimation
• Image analysis
• Image enhancement
• Image processing
• Image quality
• Image reconstruction
• Image segmentation
• Imaging systems
• Medical images
• Medical imaging
• Neural networks
• Pattern recognition
• Reconstruction
• Segmentation methods
• Semantics
• Signal processing
• Support Vector Machines (SVM)

• University of Leeds, Leeds, UK

  Alejandro F. Frangi

• King’s College London, London, UK

  Julia A. Schnabel

• University of Pennsylvania, Philadelphia, USA

  Christos Davatzikos

• Universidad de Valladolid, Valladolid, Spain

  Carlos Alberola-López

• Queen’s University, Kingston, Canada

  Gabor Fichtinger

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