Computed tomography image segmentation of irregular cerebral hemorrhage lesions based on improved U-Net

Yu-long, Yuan; Zeng, Li; Tu, Westblom; Zhu, Yiqian

doi:10.1016/j.jrras.2023.100638

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Previous studies have demonstrated the effectiveness of CNNs in detecting and segmenting intracranial hemorrhage. For example, W. Kuo et al [14] trained a fully convolutional neural network to localize abnormalities in head CT scans, while Y. Yuan et al [15] utilized a CNN based on the U-Net structure for segmenting irregular cerebral hemorrhage images. These advancements in deep learning hold the potential to enhance the efficiency and accuracy of TBI diagnosis, offering valuable support to healthcare professionals in their clinical practice.…”

Section: Advanced Imaging Techniques and The Role Of Deep Learningmentioning

confidence: 99%

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

Hoang,

Pham,

Trinh

et al. 2024

J. Imaging

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Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.

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Section: Advanced Imaging Techniques and The Role Of Deep Learningmentioning

confidence: 99%

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

Hoang,

Pham,

Trinh

et al. 2024

J. Imaging

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“…The authors of paper [23] improved the U-Net model for better segmentation of irregular intracranial hemorrhage lesions in CT images. They introduced a residual octave convolution module and a mixed attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…However, there is a scarcity of research, specifically on subdural hematomas, which can be attributed to the complexity of segmentation, with only a few works such as [16,18] addressing this area. In addition, U-Net architecture is predominantly used in these studies [17,19,20,23], while CycleGAN [21], EfficientNet and DeepMedic [22] are also employed. Some studies have also explored the approach to recognize hematomas [18,24].…”

Section: Related Workmentioning

confidence: 99%

AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images

Petrov,

Kashevnik,

Haleev

et al. 2024

Sensors

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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.

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“…Recently, image segmentation based on deep learning has become one of the main image segmentation methods. Different variations of U-Net architecture have been commonly used for detecting various kinds of lesions like skin [ 13 , 14 ], lungs [ 15 ], brain [ 16 ], etc. Another study presented a two-stage deep learning method for accurately segmenting skin lesions from dermoscopic images based on YOLO–DeepLab networks [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision

Bery,

Brown-Brandl,

Jones

et al. 2023

Animals

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Shoulder sores predominantly arise in breeding sows and often result in untimely culling. Reported prevalence rates vary significantly, spanning between 5% and 50% depending upon the type of crate flooring inside a farm, the animal’s body condition, or an existing injury that causes lameness. These lesions represent not only a welfare concern but also have an economic impact due to the labor needed for treatment and medication. The objective of this study was to evaluate the use of computer vision techniques in detecting and determining the size of shoulder lesions. A Microsoft Kinect V2 camera captured the top-down depth and RGB images of sows in farrowing crates. The RGB images were collected at a resolution of 1920 × 1080. To ensure the best view of the lesions, images were selected with sows lying on their right and left sides with all legs extended. A total of 824 RGB images from 70 sows with lesions at various stages of development were identified and annotated. Three deep learning-based object detection models, YOLOv5, YOLOv8, and Faster-RCNN, pre-trained with the COCO and ImageNet datasets, were implemented to localize the lesion area. YOLOv5 was the best predictor as it was able to detect lesions with an mAP@0.5 of 0.92. To estimate the lesion area, lesion pixel segmentation was carried out on the localized region using traditional image processing techniques like Otsu’s binarization and adaptive thresholding alongside DL-based segmentation models based on U-Net architecture. In conclusion, this study demonstrates the potential of computer vision techniques in effectively detecting and assessing the size of shoulder lesions in breeding sows, providing a promising avenue for improving sow welfare and reducing economic losses.

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Computed tomography image segmentation of irregular cerebral hemorrhage lesions based on improved U-Net

Cited by 3 publications

References 21 publications

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images

Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision

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