Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914675237584896 |
|---|---|
| author | 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. Linehan, W. Marston Turkbey, Baris Malayeri, Ashkan A. |
| author_facet | 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. Linehan, W. Marston Turkbey, Baris Malayeri, Ashkan A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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. Linehan, W. Marston Turkbey, Baris Malayeri, Ashkan A. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.05817 |