Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in ...
EL-HARIRI, Houssam, Luis A Souto Maior NETO, Petra CIMFLOVÁ, Fouzi BALA, Rotem GOLAN, Alireza SOJOUDI, Chris DUSZYNSKI, Ibukun ELEBUTE, Seyed Hossein MOUSAVI, Wu QIU and Bijoy K MENON. Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with Acute Ischemic Stroke. Computers in Biology and Medicine. OXFORD: PERGAMON-ELSEVIER SCIENCE LTD, 2022, vol. 141, February 2022, p. 1-7. ISSN 0010-4825. Available from: https://dx.doi.org/10.1016/j.compbiomed.2021.105033. |
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Original name | Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with Acute Ischemic Stroke |
Authors | EL-HARIRI, Houssam (guarantor), Luis A Souto Maior NETO, Petra CIMFLOVÁ (203 Czech Republic, belonging to the institution), Fouzi BALA, Rotem GOLAN, Alireza SOJOUDI, Chris DUSZYNSKI, Ibukun ELEBUTE, Seyed Hossein MOUSAVI, Wu QIU and Bijoy K MENON. |
Edition | Computers in Biology and Medicine, OXFORD, PERGAMON-ELSEVIER SCIENCE LTD, 2022, 0010-4825. |
Other information | |
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Original language | English |
Type of outcome | Article in a journal |
Field of Study | 30224 Radiology, nuclear medicine and medical imaging |
Country of publisher | United Kingdom of Great Britain and Northern Ireland |
Confidentiality degree | is not subject to a state or trade secret |
WWW | URL |
Impact factor | Impact factor: 7.700 |
RIV identification code | RIV/00216224:14110/22:00128306 |
Organization unit | Faculty of Medicine |
Doi | http://dx.doi.org/10.1016/j.compbiomed.2021.105033 |
UT WoS | 000788076100001 |
Keywords in English | Machine learning; Deep learning; Computer vision; Segmentation; Neurovascular imaging; Computed tomography; Acute ischemic stroke; Brain lesion |
Tags | 14110119, rivok |
Tags | International impact, Reviewed |
Changed by | Changed by: Mgr. Tereza Miškechová, učo 341652. Changed: 27/1/2023 08:49. |
Abstract |
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Identifying the presence and extent of early ischemic changes (EIC) on Non-Contrast Computed Tomography (NCCT) is key to diagnosing and making time-sensitive treatment decisions in patients that present with Acute Ischemic Stroke (AIS). Segmenting EIC on NCCT is however a challenging task. In this study, we investigated a 3D CNN based on nnU-Net, a self-adapting CNN technique that has become the state-of-the-art in medical image segmentation, for segmenting EIC in NCCT of AIS patients. We trained and tested this model on a sizeable and heterogenous dataset of 534 patients, split into 438 for training and validation and 96 for testing. On this test set, we additionally assessed the inter-rater performance by comparing the proposed approach against two reference segmentation annotations by expert neuroradiologist readers, using this as the benchmark against which to compare our model. In terms of spatial agreement, we report median Dice Similarity Coefficients (DSCs) of 39.8% for the model vs. Reader-1, 39.4% for the model vs. Reader-2, and 55.6% for Reader-2 vs. Reader-1. In terms of lesion volume agreement, we report Intraclass Correlation Coefficients (ICCs) of 83.4% for model vs. Reader-1, 80.4% for model vs. Reader-2, and 94.8% for Reader-2 vs. Reader-1. Based on these results, we conclude that our model performs well relative to expert human performance and therefore may be useful as a decision-aid for clinicians. |
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