LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a\n Probabilistic Lymph Node Atlas Article Swipe
Sofija Engelson
,
Jan Ehrhardt
,
Timo Kepp
,
Joshua Niemeijer
,
Heinz Handels
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.59275/j.melba.2024-009
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.59275/j.melba.2024-009
The evaluation of lymph node metastases plays a crucial role in achieving\nprecise cancer staging, influencing subsequent decisions regarding treatment\noptions. Lymph node detection poses challenges due to the presence of unclear\nboundaries and the diverse range of sizes and morphological characteristics,\nmaking it a resource-intensive process. As part of the LNQ 2023 MICCAI\nchallenge, we propose the use of anatomical priors as a tool to address the\nchallenges that persist in mediastinal lymph node segmentation in combination\nwith the partial annotation of the challenge training data. The model ensemble\nusing all suggested modifications yields a Dice score of 0.6033 and segments\n57% of the ground truth lymph nodes, compared to 27% when training on CT only.\nSegmentation accuracy is improved significantly by incorporating a\nprobabilistic lymph node atlas in loss weighting and post-processing. The\nlargest performance gains are achieved by oversampling fully annotated data to\naccount for the partial annotation of the challenge training data, as well as\nadding additional data augmentation to address the high heterogeneity of the CT\nimages and lymph node appearance. Our code is available at\nhttps://github.com/MICAI-IMI-UzL/LNQ2023.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.03984
- https://arxiv.org/pdf/2406.03984
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399455038
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399455038Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.59275/j.melba.2024-009Digital Object Identifier
- Title
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LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a\n Probabilistic Lymph Node AtlasWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-06Full publication date if available
- Authors
-
Sofija Engelson, Jan Ehrhardt, Timo Kepp, Joshua Niemeijer, Heinz HandelsList of authors in order
- Landing page
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https://arxiv.org/abs/2406.03984Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2406.03984Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2406.03984Direct OA link when available
- Concepts
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Atlas (anatomy), Mediastinal lymph node, Lymph node, Probabilistic logic, Segmentation, Node (physics), Computer science, Medicine, Radiology, Artificial intelligence, Internal medicine, Anatomy, Physics, Metastasis, Cancer, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.only.\nSegmentation | 107 |
| abstract_inverted_index.treatment\noptions. | 18 |
| abstract_inverted_index.unclear\nboundaries | 29 |
| abstract_inverted_index.characteristics,\nmaking | 38 |
| abstract_inverted_index.at\nhttps://github.com/MICAI-IMI-UzL/LNQ2023.\n | 165 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.18719526 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |