TCNet: Texture and Contour-Aware Model for Bone Marrow Smear Region of Interest Selection

Bone marrow smear cell morphology is the quantitative analysis of bone marrow cell images. Due to the cell overlap and adhesion in bone marrow smears, it is essential to select uniformly distributed and clear sections as regions of interest (ROIs). However, current ROI selection models have not considered the characteristics of bone marrow smears, resulting in poor performance in practical applications. By comparing bone marrow smear ROIs and non-ROIs, we have identified significant differences in fundamental features, such as texture and contour. Therefore, we propose a texture and contour-aware bone marrow smear ROI selection model (TCNet). Inspired by multi-task learning, this model enhances its feature extraction capabilities for texture and contour by constructing different prediction modules to learn feature representations of texture and contour, and applying multi-level deep supervision with pseudo labels. To validate the effectiveness of the proposed method, we evaluate it on a self-built dataset. Experimental results show that the proposed model achieves a 2.22% improvement in classification accuracy compared to the baseline model. In addition, we verify the proposed module’s generalizability by testing it on different backbone networks, and the results demonstrate its strong universality.