Windowing (CT)

Baba, Yahya; Murphy, Andrew

doi:10.53347/rid-52108

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…To suppress noise and irrelevant bright intensities (e.g., bones), and to demonstrate individual anatomy and pathology in lung ROI, we preprocessed the lung ROI through windowing. 16 During windowing, the window width (WW set to 1500 HU), described as the range of CT numbers and the window level (WL set to −500 HU), described as the midpoint of the range of the CT numbers are adjusted to alter the slice contrast and brightness respectively for the evaluation of lung parenchyma 17 ( Appendix p 9).…”

Section: Methodsmentioning

confidence: 99%

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

Batra,

Nathany,

Nath

et al. 2024

The Lancet Regional Health - Southeast Asia

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Section: Methodsmentioning

confidence: 99%

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

Batra,

Nathany,

Nath

et al. 2024

The Lancet Regional Health - Southeast Asia

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“…However, the default HU range usually provides poor contrast between normal brain tissue and ICH tissue. Hence, the contrast of each CT slice is adjusted through a process known as Windowing (Contrast Stretching) [26]. In this process, the window width and window level of each CT slice are adjusted.…”

Section: A Data Preprocessingmentioning

confidence: 99%

Two-Stage Approach to Intracranial Hemorrhage Segmentation From Head CT Images

Rajapakse,

Hung How,

Hao Chan

et al. 2024

IEEE Access

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Intracranial hemorrhage (ICH) is an emergency and a potentially life-threatening condition. Automated segmentation of ICH from head CT images can provide clinicians with volumetric measures that can be used for diagnosis and decision support for treatment procedures. Existing solutions typically involve training deep learning models to perform segmentation directly on the whole CT image. However, datasets with segmentation masks are typically very small in comparison with datasets with bounding boxes. Thus, we propose a two-stage approach that utilizes both bounding boxes and segmentation masks to help improve segmentation performance. In the first stage, ICH regions are detected and localized with bounding boxes surrounding the lesion by using a supervised YOLOv5 object detector. In the second stage, the localized ICH foreground is automatically segmented using TransDeepLab, an attention-based transformer network. Although we utilize both ground-truth bounding boxes and segmentation masks, different datasets can be used to train each stage. There is no requirement for pairing up bounding boxes and segmentation masks to train the model. Since bounding box annotations are available in larger quantities than segmentation masks, our approach allows these large datasets of bounding boxes to be used to improve ICH segmentation performance. On our dataset of segmentation masks, we demonstrated that our proposed twostage YOLOv5 + TransDeepLab model outperformed segmentation methods such as SegResNet by 8% in terms of Dice score. Given ground truth bounding boxes, a Dice score of 0.769 is achieved, outperforming state-of-the-art methods such as nnU-Net. In sum, our proposed two-stage approach produces more accurate binary segmentation of ICH for neuroradiologists and these improved measurements could potentially aid their clinical decision making process.

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“…To optimize the visual representation of these images, we employed windowing techniques, specifically setting the window center to 500 and the window width to 2000. This strategic choice resulted in a transformation of the image's appearance, highlighting specific anatomical structures of interest with enhanced clarity [18,19,20]. These adjustments contributed to an overall improvement in the visual quality of the images, a crucial aspect of our data preparation process.…”

Section: B Data Preparationmentioning

confidence: 99%

Cervical Spine Fracture Detection and Classification Using Two-Stage Deep Learning Methodology

Yaseen,

Ali,

Ali

et al. 2024

IEEE Access

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Cervical spine fractures are a medical emergency that can cause permanent paralysis and even death. Traditional fracture detection techniques, such as manual radiography image interpretation, are timeconsuming and prone to human error. Deep learning algorithms have shown promising results in various medical imaging applications i.e., disease diagnosis, including fracture detection of bones. In this study, we propose a two-stage approach for detecting cervical spine fractures. The first stage employs a convolutional neural network (CNN) model to determine the presence or absence of a fracture in the cervical spine, using a dataset of cervical spine Computed Tomography (CT) scan images as well as Grad-CAM for enhanced visualization and interpretation. In the second stage, our focus shifts to specific vertebrae within the cervical spine. To accomplish this task, we trained and evaluated the performance of the YOLOv5 and YOLOv8 models with 9170 images consisting of seven vertebrae. The detection results of both YOLO versions are compared and evaluated. The precision, recall, mAP50, and mAP50-90 were 0.900, 0.890, 0.935, 0.872, respectively. The results of this research demonstrate the potential of deep learning-based approaches for cervical spine fracture detection. By automating the detection process, these algorithms can assist radiologists and healthcare professionals in making accurate and timely diagnoses, leading to improved patient outcomes. INDEX TERMSCervical spine, computed tomography (CT), computer vision, deep learning, medical image, YOLO.FIGURE 1. Anatomy of Cervical Spine.

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Windowing (CT)

Cited by 9 publications

References 2 publications

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

Two-Stage Approach to Intracranial Hemorrhage Segmentation From Head CT Images

Cervical Spine Fracture Detection and Classification Using Two-Stage Deep Learning Methodology

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