Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation

With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we evaluate SAM's performance in zero-shot and fine-tuned scenarios on histopathology data, with a focus on interactive segmentation. Additionally, we compare SAM with other state-of-the-art interactive models to assess its practical potential and evaluate its generalization capability with domain adaptability. In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability. To improve SAM's limited local refinement ability and to enhance prompt stability while preserving its core strengths, we propose a modification of SAM's decoder. The experimental results suggest that the proposed modification is effective to make SAM useful for interactive histology image segmentation. The code is available at \url{https://github.com/hvcl/SAM_Interactive_Histopathology}

Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization

Current state-of-the-art supervised deep learning-based segmentation approaches have demonstrated superior performance in medical image segmentation tasks. However, such supervised approaches require fully annotated pixel-level ground-truth labels, which are labor-intensive and time-consuming to acquire. Recently, Scribble2Label (S2L) demonstrated that using only a handful of scribbles with self-supervised learning can generate accurate segmentation results without full annotation. However, owing to the relatively small size of scribbles, the model is prone to overfit and the results may be biased to the selection of scribbles. In this work, we address this issue by employing a novel multiscale contrastive regularization term for S2L. The main idea is to extract features from intermediate layers of the neural network for contrastive loss so that structures at various scales can be effectively separated. To verify the efficacy of our method, we conducted ablation studies on well-known datasets, such as Data Science Bowl 2018 and MoNuSeg. The results show that the proposed multiscale contrastive loss is effective in improving the performance of S2L, which is comparable to that of the supervised learning segmentation method.

DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets

Nuclei segmentation and classification is a significant process in pathology image analysis. Deep learning-based approaches have greatly contributed to the higher accuracy of this task. However, those approaches suffer from the imbalanced nuclei data composition, which shows lower classification performance on the rare nuclei class. In this paper, we propose a realistic data synthesis method using a diffusion model. We generate two types of virtual patches to enlarge the training data distribution, which is for balancing the nuclei class variance and for enlarging the chance to look at various nuclei. After that, we use a semantic-label-conditioned diffusion model to generate realistic and high-quality image samples. We demonstrate the efficacy of our method by experiment results on two imbalanced nuclei datasets, improving the state-of-the-art networks. The experimental results suggest that the proposed method improves the classification performance of the rare type nuclei classification, while showing superior segmentation and classification performance in imbalanced pathology nuclei datasets.