Automatic detection of nostril and key markers in images

Abstract

In the medical field, one of complex challenges in surgery involving the skin, cartilage, mucosa, and the skeletal platform is the cleft nasal deformity. The causes of deformity are nasal formed from improper fusion of the medial and lateral nasal prominences with the maxillary prominence during embryologic development. Primary rhinoplasty or first time of cleft nose repair is the nasal surgery for repairing the cleft nasal deformity. In post cheiloplasty, the primary rhinoplasty patients must use nasal splint for supporting surgical wound. However, regular nasal splints are expensive and only come with default nasal size. This limits nasal splint from supporting specific patient cases such as children. The goal of this study is to implement a program that can automatically detect the nostril and green marker in patient images for creating custom-made 3D nasal splints. The proposed method is created utilizing the CNN model. YOLO architecture is utilized because it is the one of several CNN architectures that works well with the face recognition task. The YOLOV5, YOLOV8, and YOLO-NAS are attempted and compared in the training phase. The model with a highest performance is selected, and fine-tuned for adjusting to be compatible with a patient dataset. The fine-tuned YOLOV8 reaches a mAP with 99.5%. The predicted images from fine-tuned model are used to perform body part segmentation like Otsu's thresholding and discover contour to locate essential features like the green marker and nostril in ellipse and bounding boxes. The nostril distance is calculated using the bounding box and ellipse. The columella distance or nostril gap is measured from the distance between nostrils in YOLO predicted label. Both distances are converted into centimeter scale and evaluated with ground truth value in volunteer and patient cases from nasal expert for inspecting efficiency between bounding box, ellipse, and size of marker. The result proves that the proposed model can detect crucial features with mean absolute error 0.102. Furthermore, the proposed model indicates that marker size has no effect on detection and distance, according to the findings of the experiments.

Keywords

• Deep learning
• CNN
• Nostril detection
• Facial features
• Otsu thresholding