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Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such…

Computer Vision and Pattern Recognition · Computer Science 2019-01-23 Maayan Frid-Adar , Idit Diamant , Eyal Klang , Michal Amitai , Jacob Goldberger , Hayit Greenspan

Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural networks (CNN) to detect liver metastases.…

Image and Video Processing · Electrical Eng. & Systems 2019-10-16 Mariëlle J. A. Jansen , Hugo J. Kuijf , Maarten Niekel , Wouter B. Veldhuis , Frank J. Wessels , Max A. Viergever , Josien P. W. Pluim

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are…

Computer Vision and Pattern Recognition · Computer Science 2018-07-23 Guotai Wang , Wenqi Li , Maria A. Zuluaga , Rosalind Pratt , Premal A. Patel , Michael Aertsen , Tom Doel , Anna L. David , Jan Deprest , Sebastien Ourselin , Tom Vercauteren

Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network…

Computer Vision and Pattern Recognition · Computer Science 2018-11-02 Avi Ben-Cohen , Roey Mechrez , Noa Yedidia , Hayit Greenspan

Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. This task, however, is very challenging since manual segmentation is prohibitively time-consuming,…

Computer Vision and Pattern Recognition · Computer Science 2020-01-24 Vatsal Agarwal , Youbao Tang , Jing Xiao , Ronald M. Summers

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of…

Computer Vision and Pattern Recognition · Computer Science 2018-06-01 Sergi Valverde , Mostafa Salem , Mariano Cabezas , Deborah Pareto , Joan C. Vilanova , Lluís Ramió-Torrentà , Àlex Rovira , Joaquim Salvi , Arnau Oliver , Xavier Lladó

Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning. In an adaptive radiotherapy setting, updated contours need to be generated based on daily imaging. In this work, we leverage…

Image and Video Processing · Electrical Eng. & Systems 2020-02-19 Mohamed S. Elmahdy , Tanuj Ahuja , U. A. van der Heide , Marius Staring

With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagno- sis, and medical intervention. Since manual anno- tation su ers limited…

Computer Vision and Pattern Recognition · Computer Science 2018-03-14 Xiaowei Xu , Qing Lu , Yu Hu , Lin Yang , Sharon Hu , Danny Chen , Yiyu Shi

Characterizing blood vessels in digital images is important for the diagnosis of many types of diseases as well as for assisting current researches regarding vascular systems. The automated analysis of blood vessels typically requires the…

Image and Video Processing · Electrical Eng. & Systems 2023-01-12 Matheus V. da Silva , Julie Ouellette , Baptiste Lacoste , Cesar H. Comin

This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and validation, as well as…

Image and Video Processing · Electrical Eng. & Systems 2020-01-29 Georg Hille , Johannes Steffen , Max Dünnwald , Mathias Becker , Sylvia Saalfeld , Klaus Tönnies

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to…

Computer Vision and Pattern Recognition · Computer Science 2016-11-01 Mohsen Ghafoorian , Nico Karssemeijer , Tom Heskes , Inge van Uden , Clara Sanchez , Geert Litjens , Frank-Erik de Leeuw , Bram van Ginneken , Elena Marchiori , Bram Platel

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create…

Machine Learning · Computer Science 2021-04-13 Zongwei Zhou , Jae Y. Shin , Suryakanth R. Gurudu , Michael B. Gotway , Jianming Liang

Automated digital histopathology image segmentation is an important task to help pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer subtypes, pathologists usually change the magnification of whole-slide…

Computer Vision and Pattern Recognition · Computer Science 2019-04-15 Hiroki Tokunaga , Yuki Teramoto , Akihiko Yoshizawa , Ryoma Bise

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image…

Computer Vision and Pattern Recognition · Computer Science 2016-02-11 Hoo-Chang Shin , Holger R. Roth , Mingchen Gao , Le Lu , Ziyue Xu , Isabella Nogues , Jianhua Yao , Daniel Mollura , Ronald M. Summers

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are…

Image and Video Processing · Electrical Eng. & Systems 2020-11-05 Ran Gu , Guotai Wang , Tao Song , Rui Huang , Michael Aertsen , Jan Deprest , Sébastien Ourselin , Tom Vercauteren , Shaoting Zhang

Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes. Recent contrastive learning methods encourage similar global representations…

Computer Vision and Pattern Recognition · Computer Science 2023-10-20 Zhangsihao Yang , Mengwei Ren , Kaize Ding , Guido Gerig , Yalin Wang

Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional…

Colorectal liver metastasis is one of most aggressive liver malignancies. While the definition of lesion type based on CT images determines the diagnosis and therapeutic strategy, the discrimination between cancerous and non-cancerous…

Computer Vision and Pattern Recognition · Computer Science 2019-01-29 Francisco Perdigon Romero , Andre Diler , Gabriel Bisson-Gregoire , Simon Turcotte , Real Lapointe , Franck Vandenbroucke-Menu , An Tang , Samuel Kadoury

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a…

Computer Vision and Pattern Recognition · Computer Science 2017-06-05 Nima Tajbakhsh , Jae Y. Shin , Suryakanth R. Gurudu , R. Todd Hurst , Christopher B. Kendall , Michael B. Gotway , Jianming Liang

Medical image segmentation plays a vital role in various clinical applications, enabling accurate delineation and analysis of anatomical structures or pathological regions. Traditional CNNs have achieved remarkable success in this field.…

Image and Video Processing · Electrical Eng. & Systems 2024-04-18 Seyed M. R. Modaresi , Aomar Osmani , Mohammadreza Razzazi , Abdelghani Chibani
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