LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a 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-f95c
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.59275/j.melba.2024-f95c
The evaluation of lymph node metastases plays a crucial role in achieving precise cancer staging, which in turn influences subsequent decisions regarding treatment options. The detection of lymph nodes poses challenges due to the presence of unclear boundaries and the diverse range of sizes and morphological characteristics, making it a resource-intensive process. As part of the LNQ 2023 MICCAI challenge, we propose the use of anatomical priors as a tool to address the challenges that persist in automatic mediastinal lymph node segmentation in combination with the partial annotation of the challenge training data. The model ensemble using all suggested modifications yields a Dice score of 0.6033 and segments 57% of the ground truth lymph nodes, compared to 27% when training on CT only. Segmentation accuracy is improved significantly by incorporating a probabilistic lymph node atlas in loss weighting and post-processing. The largest performance gains are achieved by oversampling fully annotated data to account for the partial annotation of the challenge training data, as well as adding additional data augmentation to address the high heterogeneity of the CT images and lymph node appearance. Our code is available at https://github.com/MICAI-IMI-UzL/LNQ2023.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.59275/j.melba.2024-f95c
- https://www.melba-journal.org/pdf/2024:009.pdf
- OA Status
- bronze
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4397290563
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4397290563Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.59275/j.melba.2024-f95cDigital Object Identifier
- Title
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LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node AtlasWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-19Full publication date if available
- Authors
-
Sofija Engelson, Jan Ehrhardt, Timo Kepp, Joshua Niemeijer, Heinz HandelsList of authors in order
- Landing page
-
https://doi.org/10.59275/j.melba.2024-f95cPublisher landing page
- PDF URL
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https://www.melba-journal.org/pdf/2024:009.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://www.melba-journal.org/pdf/2024:009.pdfDirect OA link when available
- Concepts
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Mediastinal lymph node, Atlas (anatomy), Lymph node, Probabilistic logic, Lymph, Segmentation, Computer science, Medicine, Radiology, Artificial intelligence, Pathology, Internal medicine, Anatomy, Metastasis, CancerTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2138626541, https://openalex.org/W2951970475, https://openalex.org/W2146653397, https://openalex.org/W317170363, https://openalex.org/W3112701542, https://openalex.org/W4291238401, https://openalex.org/W4310330006, https://openalex.org/W3153842321, https://openalex.org/W3100711616, https://openalex.org/W4291653302, https://openalex.org/W2022181036, https://openalex.org/W4393570590, https://openalex.org/W3129863258, https://openalex.org/W3085109610, https://openalex.org/W4221161877, https://openalex.org/W4399674921, https://openalex.org/W2019607817, https://openalex.org/W2811374795, https://openalex.org/W2085370032, https://openalex.org/W2964098128, https://openalex.org/W4379031951, https://openalex.org/W2963521553, https://openalex.org/W2794420370 |
| referenced_works_count | 23 |
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| abstract_inverted_index.CT | 121, 176 |
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| abstract_inverted_index.href='https://github.com/MICAI-IMI-UzL/LNQ2023'>https://github.com/MICAI-IMI-UzL/LNQ2023</a>. | 188 |
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