  Nuclei instance segmentation is crucial in oncological diagnosis and cancer
pathology research. H&E stained images are commonly used for medical diagnosis,
but pre-processing is necessary before using them for image processing tasks.
Two principal pre-processing methods are formalin-fixed paraffin-embedded
samples (FFPE) and frozen tissue samples (FS). While FFPE is widely used, it is
time-consuming, while FS samples can be processed quickly. Analyzing H&E
stained images derived from fast sample preparation, staining, and scanning can
pose difficulties due to the swift process, which can result in the degradation
of image quality. This paper proposes a method that leverages the unique
optical characteristics of H&E stained images. A three-branch U-Net
architecture has been implemented, where each branch contributes to the final
segmentation results. The process includes applying watershed algorithm to
separate overlapping regions and enhance accuracy. The Triple U-Net
architecture comprises an RGB branch, a Hematoxylin branch, and a Segmentation
branch. This study focuses on a novel dataset named CryoNuSeg. The results
obtained through robust experiments outperform the state-of-the-art results
across various metrics. The benchmark score for this dataset is AJI 52.5 and PQ
47.7, achieved through the implementation of U-Net Architecture. However, the
proposed Triple U-Net architecture achieves an AJI score of 67.41 and PQ of
50.56. The proposed architecture improves more on AJI than other evaluation
metrics, which further justifies the superiority of the Triple U-Net
architecture over the baseline U-Net model, as AJI is a more strict evaluation
metric. The use of the three-branch U-Net model, followed by watershed
post-processing, significantly surpasses the benchmark scores, showing
substantial improvement in the AJI score
