Weakly supervised real-time instance segmentation for ultrasound images of median nerves

Medical ultrasound technology has garnered significant attention in recent years, with Ultrasound-guided regional anesthesia (UGRA) and carpal tunnel diagnosis (CTS) being two notable examples. Instance segmentation, based on deep learning approaches, is a promising choice to support the analysis of ultrasound data. However, many instance segmentation models cannot achieve the requirement of ultrasound technology e.g. real-time. Moreover, fully supervised instance segmentation models require large numbers of images and corresponding mask annotations for training, which can be time-consuming and labor-intensive in the case of medical ultrasound data. This paper proposes a novel weakly supervised framework, CoarseInst, to achieve real-time instance segmentation of ultrasound images with only box annotations. CoarseInst not only improves the network structure, but also proposes a two-stage "coarse-to-fine" training strategy. Specifically, median nerves are used as the target application for UGRA and CTS. CoarseInst consists of two stages, with pseudo mask labels generated in the coarse mask generation stage for self-training. An object enhancement block is incorporated to mitigate the performance loss caused by parameter reduction in this stage. Additionally, we introduce a pair of loss functions, the amplification loss, and the deflation loss, that work together to generate the masks. A center area mask searching algorithm is also proposed to generate labels for the deflation loss. In the self-training stage, a novel self-feature similarity loss is designed to generate more precise masks. Experimental results on a practical ultrasound dataset demonstrate that CoarseInst could achieve better performance than some state-of-the-art fully supervised works.

Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy

The overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker in therapeutic effects for metastatic breast cancer. Accurate HER2 testing is critical for determining the most suitable treatment for patients. Fluorescent in situ hybridization (FISH) and dual in situ hybridization (DISH) have been recognized as FDA-approved methods to determine HER2 overexpression. However, analysis of HER2 overexpression is challenging. Firstly, the boundaries of cells are often unclear and blurry, with large variations in cell shapes and signals, making it challenging to identify the precise areas of HER2-related cells. Secondly, the use of sparsely labeled data, where some unlabeled HER2-related cells are classified as background, can significantly confuse fully supervised AI learning and result in unsatisfactory model outcomes. In this study, we present a weakly supervised Cascade R-CNN (W-CRCNN) model to automatically detect HER2 overexpression in HER2 DISH and FISH images acquired from clinical breast cancer samples. The experimental results demonstrate that the proposed W-CRCNN achieves excellent results in identification of HER2 amplification in three datasets, including two DISH datasets and a FISH dataset. For the FISH dataset, the proposed W-CRCNN achieves an accuracy of 0.970±0.022, precision of 0.974±0.028, recall of 0.917±0.065, F1-score of 0.943±0.042 and Jaccard Index of 0.899±0.073. For DISH datasets, the proposed W-CRCNN achieves an accuracy of 0.971±0.024, precision of 0.969±0.015, recall of 0.925±0.020, F1-score of 0.947±0.036 and Jaccard Index of 0.884±0.103 for dataset 1, and an accuracy of 0.978±0.011, precision of 0.975±0.011, recall of 0.918±0.038, F1-score of 0.946±0.030 and Jaccard Index of 0.884±0.052 for dataset 2, respectively. In comparison with the benchmark methods, the proposed W-CRCNN significantly outperforms all the benchmark approaches in identification of HER2 overexpression in FISH and DISH datasets (p<0.05). With the high degree of accuracy, precision and recall , the results show that the proposed method in DISH analysis for assessment of HER2 overexpression in breast cancer patients has significant potential to assist precision medicine.