AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images
This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05–0...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/721 |
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| author | Andrey Petrov Alexey Kashevnik Mikhail Haleev Ammar Ali Arkady Ivanov Konstantin Samochernykh Larisa Rozhchenko Vasiliy Bobinov |
| author_facet | Andrey Petrov Alexey Kashevnik Mikhail Haleev Ammar Ali Arkady Ivanov Konstantin Samochernykh Larisa Rozhchenko Vasiliy Bobinov |
| author_sort | Andrey Petrov |
| collection | DOAJ |
| description | This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05–0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models’ efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage. |
| first_indexed | 2024-03-08T03:50:13Z |
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| id | doaj.art-dbab95a1d96d4a42b5931b82d1a5e503 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:50:13Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dbab95a1d96d4a42b5931b82d1a5e5032024-02-09T15:21:38ZengMDPI AGSensors1424-82202024-01-0124372110.3390/s24030721AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography ImagesAndrey Petrov0Alexey Kashevnik1Mikhail Haleev2Ammar Ali3Arkady Ivanov4Konstantin Samochernykh5Larisa Rozhchenko6Vasiliy Bobinov7Polenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, RussiaSt. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, RussiaSt. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, RussiaInformation Technologies and Programming Faculty, ITMO University, 197101 St. Petersburg, RussiaPolenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, RussiaPolenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, RussiaPolenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, RussiaPolenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, RussiaThis paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05–0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models’ efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage.https://www.mdpi.com/1424-8220/24/3/721hematoma segmentationcomputed tomographycomputer vision |
| spellingShingle | Andrey Petrov Alexey Kashevnik Mikhail Haleev Ammar Ali Arkady Ivanov Konstantin Samochernykh Larisa Rozhchenko Vasiliy Bobinov AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images Sensors hematoma segmentation computed tomography computer vision |
| title | AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images |
| title_full | AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images |
| title_fullStr | AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images |
| title_full_unstemmed | AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images |
| title_short | AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images |
| title_sort | ai based approach to one click chronic subdural hematoma segmentation using computed tomography images |
| topic | hematoma segmentation computed tomography computer vision |
| url | https://www.mdpi.com/1424-8220/24/3/721 |
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