Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, Proceedings

This book presents three challenges related to medical image computing and computer-assisted intervention, including the Mitosis Domain Generalization Challenge, Medical Out-of-Distribution Analysis Challenge, and Learn2Reg Challenge, which aim to improve accuracy, reproducibility, and efficiency in automated image analysis.

Format: Paperback / softback
Length: 194 pages
Publication date: 02 March 2022
Publisher: Springer Nature Switzerland AG

This book presents a collection of 18 long and 9 short papers that were submitted for the Mitosis Domain Generalization Challenge (MIDOG 2021), the Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and the Learn2Reg (L2R 2021) at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2021), originally scheduled to be held in Strasbourg, France, but transitioned to an online event due to the COVID-19 pandemic. The challenges aimed to foster the development and rigorous evaluation of algorithms that enhance the accuracy, reproducibility, and efficiency of automated image analysis in clinically relevant applications.

The papers in this volume cover a wide range of topics within the field of biomedical image analysis, including but not limited to the following:

Mitosis Domain Generalization Challenge (MIDOG 2021): This challenge aimed to develop algorithms that can generalize the analysis of mitosis images across different datasets and imaging modalities. The participants were tasked with developing models that can accurately classify mitotic cells and predict their features, such as shape, size, and orientation.

Medical Out-of-Distribution Analysis Challenge (MOOD 2021): This challenge focused on developing algorithms that can analyze medical images obtained from patients outside the range of training data. The participants were challenged to identify abnormal or unexpected patterns in the images that could indicate the presence of disease or other abnormalities.

Learn2Reg (L2R 2021): This challenge aimed to develop algorithms that can learn to generate regulatory regions, which are regions of interest in medical images that can be used for automated segmentation and analysis. The participants were tasked with developing models that can learn to identify and segment these regions based on their characteristics and spatial relationships.

The papers in this volume demonstrate the latest advancements in biomedical image analysis and computer-assisted intervention, showcasing the potential of these technologies to improve healthcare outcomes. The challenges also highlight the need for developing and evaluating algorithms that are robust, accurate, and reproducible, which can be used in real-world clinical settings.

The contributions of the authors to these challenges are noteworthy, and their work provides valuable insights into the state-of-the-art in biomedical image analysis. The editors of this volume have done an excellent job in organizing and presenting the papers, making it a valuable resource for researchers and practitioners in the field.

In conclusion, this book presents a collection of high-quality papers that address three challenging topics in biomedical image analysis. The challenges presented in this volume highlight the need for developing and evaluating algorithms that can enhance the accuracy, reproducibility, and efficiency of automated image analysis in clinically relevant applications. The contributions of the authors to these challenges are valuable, and the editors have done an excellent job in organizing and presenting the papers. This book will be a valuable resource for researchers and practitioners in the field of biomedical image analysis.

Weight: 320g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030972806
Edition number: 1st ed. 2022