A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments demonstrate the competitiveness of the proposed method.

MambaEviScrib: Mamba and Evidence-Guided Consistency Make CNN Work Robustly for Scribble-Based Weakly Supervised Ultrasound Image Segmentation

Segmenting anatomical structures and lesions from ultrasound images contributes to disease assessment, diagnosis, and treatment. Weakly supervised learning (WSL) based on sparse annotation has achieved encouraging performance and demonstrated the potential to reduce annotation costs. However, ultrasound images often suffer from issues such as poor contrast, unclear edges, as well as varying sizes and locations of lesions. This makes it challenging for convolutional networks with local receptive fields to extract global morphological features from the sparse information provided by scribble annotations. Recently, the visual Mamba based on state space sequence models (SSMs) has significantly reduced computational complexity while ensuring long-range dependencies compared to Transformers. Consequently, for the first time, we apply scribble-based WSL to ultrasound image segmentation and propose a novel hybrid CNN-Mamba framework. Furthermore, due to the characteristics of ultrasound images and insufficient supervision signals, existing consistency regularization often filters out predictions near decision boundaries, leading to unstable predictions of edges. Hence, we introduce the Dempster-Shafer theory (DST) of evidence to devise an Evidence-Guided Consistency (EGC) strategy, which leverages high-evidence predictions more likely to occur near high-density regions to guide low-evidence predictions potentially present near decision boundaries for optimization. During training, the collaboration between the CNN branch and the Mamba branch in the proposed framework draws inspiration from each other based on the EGC strategy. Extensive experiments on four ultrasound public datasets for binary-class and multi-class segmentation demonstrate the competitiveness of the proposed method. The scribble-annotated dataset and code will be made available on https://github.com/GtLinyer/MambaEviScrib.