Effective training of deep image segmentation models is challenging due to
the need for abundant, high-quality annotations. Generating annotations is
laborious and time-consuming for human experts, especially in medical image
segmentation. To facilitate image annotation, we introduce Physics Informed
Contour Selection (PICS) - an interpretable, physics-informed algorithm for
rapid image segmentation without relying on labeled data. PICS draws
inspiration from physics-informed neural networks (PINNs) and an active contour
model called snake. It is fast and computationally lightweight because it
employs cubic splines instead of a deep neural network as a basis function. Its
training parameters are physically interpretable because they directly
represent control knots of the segmentation curve. Traditional snakes involve
minimization of the edge-based loss functionals by deriving the Euler-Lagrange
equation followed by its numerical solution. However, PICS directly minimizes
the loss functional, bypassing the Euler Lagrange equations. It is the first
snake variant to minimize a region-based loss function instead of traditional
edge-based loss functions. PICS uniquely models the three-dimensional (3D)
segmentation process with an unsteady partial differential equation (PDE),
which allows accelerated segmentation via transfer learning. To demonstrate its
effectiveness, we apply PICS for 3D segmentation of the left ventricle on a
publicly available cardiac dataset. While doing so, we also introduce a new
convexity-preserving loss term that encodes the shape information of the left
ventricle to enhance PICS's segmentation quality. Overall, PICS presents
several novelties in network architecture, transfer learning, and
physics-inspired losses for image segmentation, thereby showing promising
outcomes and potential for further refinement.

通过物理信息轮廓选择（PICS）算法，结合蛇模型和基于区域的损失函数，迅速实现了无需依赖标注数据即可进行图像分割的三维左心室分割。

PICS in Pics: 用于快速图像分割的基于物理模型的轮廓选择

PICS in Pics: Physics Informed Contour Selection for Rapid Image  Segmentation

Echocardiography has become an indispensable clinical imaging modality for
general heart health assessment. From calculating biomarkers such as ejection
fraction to the probability of a patient's heart failure, accurate segmentation
of the heart and its structures allows doctors to plan and execute treatments
with greater precision and accuracy. However, achieving accurate and robust
left ventricle segmentation is time-consuming and challenging due to different
reasons. This work introduces a novel approach for consistent left ventricular
(LV) segmentation from sparsely annotated echocardiogram videos. We achieve
this through (1) self-supervised learning (SSL) using temporal masking followed
by (2) weakly supervised training. We investigate two different segmentation
approaches: 3D segmentation and a novel 2D superimage (SI). We demonstrate how
our proposed method outperforms the state-of-the-art solutions by achieving a
93.32% (95%CI 93.21-93.43%) dice score on a large-scale dataset
(EchoNet-Dynamic) while being more efficient. To show the effectiveness of our
approach, we provide extensive ablation studies, including pre-training
settings and various deep learning backbones. Additionally, we discuss how our
proposed methodology achieves high data utility by incorporating unlabeled
frames in the training process. To help support the AI in medicine community,
the complete solution with the source code will be made publicly available upon
acceptance.

该研究提出了一种新颖的方法，通过自监督学习和弱监督训练，在稀疏注释的超声心动图视频中实现了一致的左心室分割，相比最先进的解决方案，在效率更高的同时，取得了 93.32% 的 Dice 得分，并通过包含未标记帧在训练过程中实现高数据效用。

UniLVSeg：通过自监督时序遮罩和弱监督训练实现左室超声心动图视频的统一分割

UniLVSeg: Unified Left Ventricular Segmentation with Sparsely Annotated  Echocardiogram Videos through Self-Supervised Temporal Masking and Weakly  Supervised Training

This study investigates a curriculum-style strategy for semi-supervised CNN
segmentation, which devises a regression network to learn image-level
information such as the size of a target region. These regressions are used to
effectively regularize the segmentation network, constraining softmax
predictions of the unlabeled images to match the inferred label distributions.
Our framework is based on inequality constraints that tolerate uncertainties
with inferred knowledge, e.g., regressed region size, and can be employed for a
large variety of region attributes. We evaluated our proposed strategy for left
ventricle segmentation in magnetic resonance images (MRI), and compared it to
standard proposal-based semi-supervision strategies. Our strategy leverages
unlabeled data in more efficiently, and achieves very competitive results,
approaching the performance of full-supervision.

通过课程式策略和回归网络，基于不等式约束的半监督 CNN 分割技术在磁共振图像左心室分割方面的应用上，使用未标记的数据更高效地实现了分割网络，与标准的基于建议的半监督策略相比有着很有竞争力的结果，接近全监督的性能水平。