3D reconstruction of the Right Ventricle (RV) from echocardiograms is crucial for accurate clinical evaluation of cardiac function. However, existing methods are hindered by the complex RV anatomy and the incomplete spatial information inherent in 2D multi-view echocardiograms. Therefore, we propose RVDeformer, a sparse point cloud-guided framework for RV 3D reconstruction. RVDeformer reformulates the reconstruction task as a mesh deformation problem, learning to deform a predefined template mesh to match the target structure under the guidance of the sparse anatomical point cloud. Specifically, this framework employs the end-to-end neural network RVDeformNet to extract the features of the point cloud and template mesh for predicting the displacement of each mesh vertex. We design a point cloud-mesh fusion module that can effectively align and fuse features from the two modalities to enhance the representation ability of the model. We conduct extensive validation on a clinical dataset of 1,278 cases and demonstrate that RVDeformer outperforms existing state-of-the-art methods, achieving a Chamfer Distance (CD) of $2.24\pm 0.55$ mm, an F1-score of $0.74\pm 0.10$ at the 3 mm threshold, and a Volumetric Similarity (VS) of $91.53\pm 2.28$ %, with significant potential for clinical applications. The code is available at https://github.com/onezh95/RVDeformer.

Tensor program tuning is essential for the efficient deployment of deep neural networks. Search-based approaches have demonstrated scalability and effectiveness in automatically finding high-performance programs for specific hardware. However, the search process is often inefficient, taking hours or even days to discover optimal programs due to the exploration mechanisms guided by an accurate but slow-learned cost model. Meanwhile, the learned cost model trained on one platform cannot seamlessly adapt online to another, which we call cross-platform online unawareness. In this work, we propose Pruner and MoA-Pruner. Pruner is a ''Draft-then-Verify'' exploration mechanism that accelerates the schedule search process. Instead of applying the complex learned cost model to all explored candidates, Pruner drafts small-scale potential candidates by introducing a naive Symbol-based Analyzer (draft model), then identifies the best candidates by the learned cost model. MoA-Pruner introduces a Momentum online Adaptation strategy to address the cross-platform online unawareness.

Accurate 3D reconstruction of the right ventricle from multi-view echocardiograms is crucial for the quantitative diagnosis of cardiac diseases. However, existing methods often fail to deliver satisfactory results due to the structural complexity of the right ventricle and the sparsity of non-parallel ultrasound views. In this paper, we propose an efficient reconstruction method named Carver, which redefines 3D reconstruction of the right ventricle as a voxel-wise dense prediction task for the first time. The core idea lies in using a deep neural network to learn the end-to-end deformation from a coarse geometric convex hull to a fine structure of the right ventricle, similar to carving. To improve the reconstruction accuracy and robustness, we design a dual-aware network incorporating prior contour information to enhance learning representation. We conduct extensive experiments on an echocardiography dataset containing 1,278 instances to validate the effectiveness of the proposed method. Experimental results demonstrate that Carver outperforms existing state-of-the-art methods, achieving a Volume Similarity (VS) of 98.75%, a Dice Similarity Coefficient (DSC) of 97.80%, a Hausdorff Distance (HD) of 4.96, and a Root Mean Square Error (RMSE) of 0.013 for the ejection fraction, while maintaining considerable robustness even with sparser inputs. The code is available at https://github.com/ustclyd/Carver.

Deep learning has made remarkable medical image segmentation advancements, yet its generalization capability across tasks remains challenging. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the poor generalization capacity of task-specific models on unseen tasks, necessitating domain adaptation or fine-tuning. This typically involves data annotation and network retraining, limiting the application of deep learning in clinical practice. In this study, we propose PromptSeg, an innovative Transformer-based unified segmentation framework for general medical image segmentation tasks. PromptSeg aims to utilize the provided visual prompts to recognize task patterns and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets during inference, our method only requires a few annotated prompt pairs to understand the task and segment the query images without retraining, alleviating the need for large-scale annotation data. The experimental results demonstrate that our method outperforms existing state-of-the-art methods and exhibits high generalization capability on multiple unseen tasks. The source code is available at https://github.com/MinfanZhao/PromptSeg.