3D Segmentation in the Clinic: A Grand Challenge

By B. van Ginneken, Tobias Heimann, Martin Andreas Styner. 2007

384 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ba2b89db45305bd229a733e90d28909d42543bb9

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Citations in the corpus

CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation (2020, background): “Being one of the pioneering challenges, SLIVER07 initialized the liver benchmarking [12], [13].”
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (2017, methodology): “ResNet [15], Inception [16, 17], have gradually surpassed straightforward networks and become the main-stream model for researchers.”
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, methodology): “The use of publicly available datasets and methods is an increasing trend in the medical imaging community, for example in challenge workshops at conferences [46]–[49].”
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge (2019, background): “challenges in recent years, in many medical imaging fields, for example: liver segmentation in CT (SLIVER07) [10], brain tumor segmentation in MRI (BRATS) [11], or lung nodule detection in CT (LUNA16) [12].”
The Liver Tumor Segmentation Benchmark (LiTS) (2019, methodology): “The Segmentation of the Liver (Sliver’07) was organized in conjunction with MICCAI 2007 and offered 30 CT volumes[102].”
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge (2019, background): “images, such as the multiple sclerosis (MS) lesions [11], [12], tumor [13], or tissue [14] segmentation challenges.”
A Bayesian model of shape and appearance for subcortical brain segmentation (2011, unknown): “…segmentations across many different methods can be found as part of the ongoing " Caudate Segmentation Evaluation " (http://www.cause07.org; van Ginneken et al., 2007) which give values less than 80%, while our Dice results are often above 80%, although their results are averages over five…”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “Some methods use the output of label fusion to simply initialize a subsequent algorithm, for instance, to determine the bounding box where a segmentation method is applied (van Rikxoort et al., 2007a) or to start the evolution of an active contour (Fritscher et al., 2014; Hollensen et al., 2010).”
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018, background): “ole in advancing research in medical imaging, providing benchmarks to compare dierent approaches and leading to more impactful contributions. Early works such as DDSM [13], SLIVER07 [14] and CAUSE07 [8] triggered increasing eorts to collect new larger-scale biomedical datasets, which resulted in ove”
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation (2010, methodology): “More details of the evaluation can be found on the workshop webpage and [50].”