Summary: Medical researchers obtain knowledge about the prevention and
treatment of disability and disease using physical measurements and image data.
To assist in this endeavor, feature extraction packages are available that are
designed to collect data from the image structure. In this study, we aim to
augment current works by adding to the current mix of shape-based features. The
significance of shape-based features has been explored extensively in research
for several decades, but there is no single package available in which all
shape-related features can be extracted easily by the researcher. PyCellMech
has been crafted to address this gap. The PyCellMech package extracts three
classes of shape features, which are classified as one-dimensional, geometric,
and polygonal. Future iterations will be expanded to include other feature
classes, such as scale-space.
Availability and implementation: PyCellMech is freely available at
this https URL

医学研究人员利用物理测量和图像数据获得有关残疾和疾病预防和治疗的知识。在这项研究中，我们通过向基于形状的特征的当前混合中添加新的特征提取软件包来增强当前工作的研究。单一软件包中可以由研究人员轻松提取所有形状相关特征的重要性在研究中已经得到广泛探讨，而 PyCellMech 则填补了这一空白。PyCellMech 软件包提取了三类形状特征，分为一维、几何和多边形。未来版本将扩展到包括其他特征类别，例如尺度空间。

PyCellMech：用于医学和生物学研究的基于形状的特征提取流水线

PyCellMech: A shape-based feature extraction pipeline for use in medical  and biological studies

The physics-informed neural network (PINN) is capable of recovering partial
differential equation (PDE) coefficients that remain constant throughout the
spatial domain directly from physical measurements. In this work, we propose a
spatially dependent physics-informed neural network (SD-PINN), which enables
the recovery of coefficients in spatially-dependent PDEs using a single neural
network, eliminating the requirement for domain-specific physical expertise.
The proposed method exhibits robustness to noise owing to the incorporation of
physical constraints. It can also incorporate the low-rank assumption of the
spatial variation for the PDE coefficients to recover the coefficients at
locations without available measurements.

提出了一种基于物理的神经网络（PINN）的空间依赖型方法（SD-PINN），能够通过单个神经网络恢复空间相关的偏微分方程（PDE）系数，消除了对领域专业知识的需求。该方法通过引入物理约束具有对噪声的鲁棒性，并结合 PDE 系数空间变化的低秩假设，能够恢复没有测量数据的位置的系数。