Conference
International Symposium on Biomedical Imaging
About: International Symposium on Biomedical Imaging is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Segmentation. Over the lifetime, 6854 publications have been published by the conference receiving 88040 citations.
Topics: Image segmentation, Segmentation, Iterative reconstruction, Image registration, Scale-space segmentation
Papers published on a yearly basis
Papers
More filters
04 Apr 2018
TL;DR: The most recent edition of the dermoscopic image analysis benchmark challenge as discussed by the authors was organized to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer.
Abstract: This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.
1,419 citations
09 Jun 2011
TL;DR: Ilastik as mentioned in this paper is an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way, based on labels provided by the user through a convenient mouse interface.
Abstract: Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.
1,158 citations
28 Jun 2009
TL;DR: This paper provides two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.
Abstract: Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.
946 citations
13 Apr 2016
TL;DR: This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets and an off-line convolutional neural network to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data.
Abstract: This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and accurate imaging.
728 citations
28 Jun 2009
TL;DR: A general purpose mesh generator for creating finite-element surface or volumetric mesh from 31) binary or gray-scale medical images and demonstrates the applications of this toolbox for meshing a range of challenging geometries including complex vessel network, human brain and breast.
Abstract: We report a general purpose mesh generator for creating finite-element surface or volumetric mesh from 31) binary or gray-scale medical images. This toolbox incorporates a number of existing free mesh processing utilities and enables researchers to perform a range of mesh processing tasks for image-based mesh generation, including raw image processing, surface mesh extraction, surface re-sampling, and multi-scale/adaptive tetrahedral mesh generation. We also implemented robust algorithms for meshing open-surfaces and sub-region labeling. Atomic meshing utilities for each processing step can be accessed with simple interfaces, which can be streamlined or executed independently. The toolbox is compatible with Matlab or GNU Octave. We demonstrate the applications of this toolbox for meshing a range of challenging geometries including complex vessel network, human brain and breast.
697 citations