YOLO-HV: A fast YOLOv8-based method for measuring hemorrhage volumes

Measuring the volume of a cerebral hemorrhage is crucial for clinical diagnosis and treatment. It helps doctors assess the severity of the bleeding, guide treatment decisions, and improve patient survival rates and quality of life. However, due to the irregularity and fluid nature of the hemorrhages, existing methods struggle to segment and measure different hemorrhage instances. This paper introduces an efficient cerebral hemorrhage segmentation network, YOLO-HV, based on YOLOv8n-seg, designed for volumetric measurement of cerebral hemorrhages. To enhance the extraction of spatial feature information from irregular hemorrhagic areas, A CoordAttention mechanism is integrated into the backbone of the network. Addressing the limitations of lightweight models in training with large-scale data, a GDConv (Ghost Dynamic Convolution) module is introduced in the Neck component to replace the original C2f module. The original detection head is replaced with LGND (Lightweight Group Normalized Detection Head), enhancing positioning and classification performance of the network while additionally reducing computational costs. A Union-Find is used on a spatial level to match cross-layer instances of the same hemorrhages. Experimental results demonstrate that the YOLO-HV network achieved a F1 (F1_score) of 93.0 % and a MIoU (Mean Intersection over Union) of 87.1 %. Meanwhile, the model size has been reduced to 4.2 MB, surpassing other mainstream segmentation networks. Furthermore, the precision of volume measurement reached 93.7 %.