Multi-organ nuclei segmentation via prompt learning
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Solution Overview
Problem
Current medical image segmentation models primarily focus on single-organ nuclei segmentation and lack comprehensive learning for multi-organ cell segmentation, often requiring retraining for each additional organ and not effectively utilizing multi-modality information.
Innovation Solution
A multi-organ nuclei segmentation method based on prompt learning is introduced, which involves creating a nuclei image-text dataset and using a segmentation network model that incorporates text and image modules to perform unsupervised nuclei segmentation across multiple organs with the aid of text prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a segmentation model is retrained for each additional organ, then the segmentation ability for more organs is improved, but the computational cost and time consumption increase significantly
Solution Approach 1:
The patent applies prompt learning to enable a single segmentation model to perform multi-organ nuclei segmentation without retraining. By introducing learnable prompt parameters that encode organ-specific characteristics, the model achieves universal applicability across different organ types while maintaining efficient inference time. The prompt parameters act as adaptable interfaces between the fixed backbone network and diverse segmentation tasks.
Solution Approach 2:
The patent changes the approach from modifying entire model parameters through retraining to adjusting only prompt parameters. These prompt parameters are small, learnable vectors that are optimized for each organ type while the main network remains frozen. This parameter change strategy enables rapid adaptation to new organs with minimal computational overhead.
2Measurement precision
If multi-modality information including text and image is utilized, then comprehensive learning for target region segmentation is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces text prompts as intermediary representations that bridge visual image data and segmentation outcomes. These text prompts serve as mediators that encode organ-specific semantic information, allowing the model to leverage multi-modality information without directly processing complex multi-source data. The text prompts simplify the integration of multi-modality information while improving segmentation accuracy.
3Productivity
If segmentation models are constructed based on partially labeled datasets or specific organ datasets, then the model development speed is improved, but the segmentation performance on other organs deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-training the segmentation model on a source organ dataset and then adapting it to target organs through prompt learning. This preliminary training establishes a strong baseline capability, and the subsequent prompt-based adaptation enables rapid transfer to new organs without requiring extensive retraining or large annotated datasets for each organ type.
Data Source
AI summary
A multi-organ nuclei segmentation method based on prompt learning is provided. The multi-organ nuclei segmentation method fully mines image information based on text and image information and learns an association between semantic information and a segmentation target, thereby achieving comprehensive learning for target region segmentation. The multi-organ nuclei segmentation method learns a large amount of text and image paired knowledge from six publicly available nucleus datasets based on a clip model to acquire prior knowledge for semantic understanding of nuclei, making a model fully suitable for nuclei segmentation. The constructed model inputs images and text prompts, and utilizes text and image information to achieve nucleus recognition and accurate nuclei segmentation of six different organs, improving computational efficiency. The model can also utilize sufficient text prompts to complete accurate segmentation tasks on some unlabeled datasets, achieving practicality and scalability.