Cross-Scale Guidance Integration Transformer for Instance Segmentation in Pathology Images.

Area of Science:

• Digital pathology
• Computer-aided diagnosis
• Medical image analysis

Background:

• Accurate adenocarcinoma grading requires manual review of pathology images by pathologists.
• Manual grading faces challenges in inter-observer and intra-observer reproducibility.
• Instance segmentation of individual gland cells is crucial for automated grading but remains difficult.

Purpose of the Study:

• To develop an automated method for gland cell instance segmentation to assist in adenocarcinoma grading.
• To improve the accuracy and reproducibility of computer-assisted grading of adenocarcinoma.
• To address the challenge of segmenting individual gland cells of varying sizes.

Main Methods:

• A novel cross-scale guidance integration transformer was proposed for gland cell instance segmentation.
• The network integrates multi-scale features using a cross-scale guidance integration module.
• A decoder with mask attention utilizes integrated features for improved segmentation.

Main Results:

• The proposed method achieved state-of-the-art performance on two public gland cell datasets.
• It demonstrated superior accuracy in segmenting individual gland cells compared to existing deep learning methods.
• The approach effectively handles variations in gland cell size and morphology.

Conclusions:

• The cross-scale guidance integration transformer provides accurate gland cell segmentation.
• This method assists pathologists in computer-assisted grading of adenocarcinoma.
• The approach enhances reproducibility and efficiency in digital pathology workflows.