Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

By Tobias Heimann, B. van Ginneken, Martin Andreas Styner, Y. Arzhaeva, V. Aurich, Christian Bauer, A. Beck, Christoph Becker, R. Beichel, György Bekes and 40 more. IEEE Transactions on Medical Imaging, 2009

1,124 citations recorded by the source corpus.

Subfields: General Health Professions, Nutrition and Dietetics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/75352a06f8f39701d59c3a3a78e5cce6dd469ea9

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In General Health Professions

1. Michael David Abràmoff

In Nutrition and Dietetics

1. Adriana Romero
2. Michal Drozdzal

In Public Health, Environmental and Occupational Health

1. Michael David Abràmoff
2. Michael G. Rabbat
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Citations in the corpus

• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018, background): “Early works such as DDSM [13], SLIVER07 [14] and CAUSE07 [8] triggered increasing efforts to collect new larger-scale biomedical datasets, which resulted in over one hundred public releases (counting the entries on https://grand-challenge.org/ ) to advance medical image analysis research.”
• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “…medical image segmentation and annotation include algorithms for artery centerline extraction [61], [62], vessel segmentation and stenosis grading [63], liver segmentation [64], [65], detection of microaneurysms in digital color fundus photographs [66], and extraction of airways from CT scans [67].”
• Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge (2019, methodology): “This has been successfully applied to various tasks, such as liver segmentation [7], image registration [8], coronary calcium scoring [9], or gland segmentation in histology images [10].”
• AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? (2020, background): “deforms the points to fit to the object in a new image [1],”
• Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs (2010, background): “However, some groups may be tempted to use human readers to assist the performance of their algorithm, and therefore synchronous competitions have developed where there is a deadline for submitting results, and the competition results are announced synchronously [12].”
• elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “Open challenges, such as recently organized for liver segmentation [65] and coronary artery centerline extraction [66], would be an interesting way to compare image”
• Mel Frequency Cepstral Coefficient and its Applications: A Review (2022, background): “It is not applicable to deal with the whole of these datasets, which are used to develop automatic systems [15].”
• 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes (2016, methodology): “Details of these metrics can be found in [6].”
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields (2016, methodology): “We assessed the performance of our proposed method using the quality metrics introduced in the grand challenges for liver and lesion segmentation by [9,4].”
• Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks (2017, methodology): “Furthermore, grey level and texture based methods have been developed [1].”
• The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge (2019, background): “, 2018), but they first started to attract broader interest within the medical imaging community in 2007 when “Biomedical Grand Challenges” were first officially affiliated with the MICCAI conference in Brisbane Australia that year (Heimann et al., 2009).”
• RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans (2018, methodology): “We evaluated the performance of the proposed approach using the metrics introduced in [38].”
• Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019, background): “Running times are approximately the same across datasets, so we test them on SLIVER, with an NVIDIA TITAN Xp GPU and an Intel Xeon E5-2690 v4 CPU.”
• Extraction of Airways From CT (EXACT'09) (2012, result): “Such a property is not unique and has been noted in other comparative studies [44], [45].”