MedYOLO: A Medical Image Object Detection Framework.

التفاصيل البيبلوغرافية
العنوان:	MedYOLO: A Medical Image Object Detection Framework.
المؤلفون:	Sobek J; Department of Radiology, Mayo Clinic, Rochester, MN, USA. sobek.joseph@mayo.edu., Medina Inojosa JR; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.; Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA., Medina Inojosa BJ; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA., Rassoulinejad-Mousavi SM; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Conte GM; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Lopez-Jimenez F; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA., Erickson BJ; Department of Radiology, Mayo Clinic, Rochester, MN, USA.
المصدر:	Journal of imaging informatics in medicine [J Imaging Inform Med] 2024 Jun 06. Date of Electronic Publication: 2024 Jun 06.
Publication Model:	Ahead of Print
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
أسماء مطبوعة:	Original Publication: [Cham, Switzerland] : Springer Nature, [2024]-
مستخلص:	Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general-purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on a diverse range of structures even without hyperparameter tuning, reaching mean average precision (mAP) at intersection over union (IoU) 0.5 of 0.861 on BRaTS, 0.715 on the abdominal CT dataset, and 0.995 on the heart CT dataset. However, the models struggle with some structures, failing to converge on LIDC resulting in a mAP@0.5 of 0.0. (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
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فهرسة مساهمة:	Keywords: Computed tomography; Convolutional neural network; Deep learning; Magnetic resonance; Medical imaging; Object detection
تواريخ الأحداث:	Date Created: 20240606 Latest Revision: 20240606
رمز التحديث:	20240607
DOI:	10.1007/s10278-024-01138-2
PMID:	38844717
قاعدة البيانات:	MEDLINE

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الوصف
تدمد:	2948-2933
DOI:	10.1007/s10278-024-01138-2