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Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.
Anari, Pouria Yazdian; Obiezu, Fiona; Lay, Nathan; Firouzabadi, Fatemeh Dehghani; Chaurasia, Aditi; Golagha, Mahshid; Singh, Shiva; Homayounieh, Fatemeh; Zahergivar, Aryan; Harmon, Stephanie; Turkbey, Evrim; Gautam, Rabindra; Ma, Kevin; Merino, Maria; Jones, Elizabeth C; Ball, Mark W; Marston Linehan, W; Turkbey, Baris; Malayeri, Ashkan A.
Afiliação
  • Anari PY; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Obiezu F; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Lay N; Artificial Intelligence Resource, National Institutes of Health, USA.
  • Firouzabadi FD; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Chaurasia A; Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA.
  • Golagha M; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Singh S; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Homayounieh F; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Zahergivar A; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Harmon S; Artificial Intelligence Resource, National Institutes of Health, USA.
  • Turkbey E; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Gautam R; Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA.
  • Ma K; Artificial Intelligence Resource, National Institutes of Health, USA.
  • Merino M; Pathology Department, National Cancer Institutes, National Institutes of Health, USA.
  • Jones EC; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
  • Ball MW; Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA.
  • Marston Linehan W; Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA.
  • Turkbey B; Artificial Intelligence Resource, National Institutes of Health, USA.
  • Malayeri AA; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA.
ArXiv ; 2024 Feb 12.
Article em En | MEDLINE | ID: mdl-38903734
ABSTRACT

Introduction:

This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats.

Methods:

Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP).

Results:

The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01.

Conclusion:

Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article