LeukoSegmenter: A Double Encoder–Decoder-Based Network for Leukocyte Segmentation from Blood Smear Images

Segmentation plays a crucial role in the automated morphological analysis of blood smear images, as it directly impacts the accuracy of various operations like cell count determination and disease detection. However, existing computerized methods for cell segmentation have not been able to achieve the same level of accuracy as expert histopathologists. This is due to the inherent challenges posed by the diverse shapes, sizes, and morphological characteristics of blood cells, the presence of clumped regions, and variations in slide preparation. To address this issue, we propose a double encoder–decoder network called “LeukoSegmenter” that focuses on segmenting leukocytes and diagnosing diseases like leukemia. The LeukoSegmenter network architecture consists of two encoder–decoder pairs (EDPs) of U-Nets cascaded in series. The cascading design refines pixel-level information, enhancing feature extraction while avoiding overfitting. Each EDP adopts a modified U-Net architecture with ResNet18 as the encoder, leveraging transfer learning to extract hierarchical features efficiently. Skip connections within the U-Net preserve fine details and improve gradient flow. The decoder upscales feature maps using transposed convolutions, restoring spatial resolution and generating a coarse segmentation map in the first EDP. This output is concatenated with the original image and passed to the second EDP. This approach enables the latter EDP to specifically concentrate on leukocytes while disregarding other blood cells and debris, thereby enhancing the segmentation accuracy. The proposed method achieves DICE scores of 95.19%, 94.20%, and 95.71%; IoU of 92.68%, 94.82%, and 94.24%; precision of 96.58%, 95.29%, and 94.45%; and accuracy of 95.77%, 94.22%, and 93.55% on ALL-IDB1, LISC, and Cellavision datasets, respectively. The model also demonstrated resilience to different types of noise and blurring effects.