AOFNet: A novel cerebral hemorrhage segmentation network based on anatomical-omics feature

Lingling Fang, Yumeng Jiang, Kaining Zhu, Yang Liu

Abstract: Highlights•The proposed model applies clinical data to closely mimic real-world clinical environments, offering assistance in the diagnosis of cerebral hemorrhage diseases.•An anatomical reconstruction algorithm is utilized to extract atomical-omics feature (AOF) from clinical images, facilitating a better understanding of the characteristics of clinical hematomas.•The AOFNet model incorporates an attention mechanism tailored to clinical data features and AOF correction to mitigate feature loss, while residual blocks further enhance feature mapping during the encoding-decoding process.•The model exhibits robustness in segmenting hemorrhages with irregular shapes or complex backgrounds in clinical data, and its applicability is confirmed through evaluation on publicly available datasets.