CfP Patch-based techniques for medical imaging (PatchMI) workshop at MICCAI 2017

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Important dates

Paper submission: June 16

Author notification: July 14

Camera-ready: July 20

Workshop date: September 14

Papers are limited to 8 pages following the LNCS format

Proceed to submission


Patch-based techniques play an increasing role in the medical imaging field, with various applications in image segmentation, image de-noising, image super-resolution, super-pixel/voxel-based analysis, computer-aided diagnosis, image registration, abnormality detection and image synthesis. Dictionaries of local image patches are increasingly being used for example in the context of segmentation and computer-aided diagnosis. Patch-based dictionaries are commonly used in conjunction with pattern recognition techniques to model complex anatomies in an accurate and easy way. The patch-level representation of image content is between the global image and localized voxels. This level of representation is shown to be successful in areas such as image processing (e.g., enhancement and de-noising) as well as image feature extraction and classification (e.g., convolution kernels and convolutional neural networks).

The main aim of this workshop is to help advance the scientific research within the broad field of patch-based processing in medical imaging. This workshop will focus on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging. We hope the workshop to become a new platform for translating research from bench to bedside. We are looking for original, high-quality submissions on innovative research and development in the analysis of medical image data using patch-based techniques.


Topics of interests include but are not limited to patch-based processing dedicated to:

  • Image segmentation of anatomical structures or lesions (e.g., brain segmentation, cardiac segmentation, MS lesions detection, tumor segmentation)
  • Image enhancement (e.g., de-noising or super-resolution dedicated to fMRI, DWI, MRI or CT)
  • Computer-aided prognostic and diagnostic (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, brain diseases, liver cancer, acute disease, chronic disease, osteoporosis)
  • Mono and multimodal image registration
  • Multi-modality fusion (e.g., MRI/PET, PET/CT, projection X-ray/CT, X-ray/ultrasound) for diagnosis, image analysis and image guided interventions
  • Mono and multi modal image synthesis (e.g., synthesis of missing a modality in a database using an external library)
  • Image retrieval (e.g., context-based retrieval, lesion similarity)
  • Dynamic, functional, physiologic, and anatomic imaging
  • Super-pixel/voxel-based analysis in medical images
  • Sparse dictionary learning and sparse coding
  • Analysis of 2D, 2D+t, 3D, 3D+t and 4D and 4D+t data.

Academic objectives

An academic objective of the workshop is to bring together researchers in medical imaging to discuss new techniques using patch-based approaches and their use in clinical decision support and large cohort studies. Another objective is to explore new paradigms of the design of biomedical image analysis systems that exploit latest results in patch-based processing and exemplar-based methods. MICCAI-PMI 2017 will feature a single-track workshop with keynote speakers, technical paper presentations, poster sessions, and demonstrations of the state-of-the-art technics and concepts that are applied to analyzing medical images