来自光学相干断层造影血管造影（OctA）的en面部图像的变形缺陷区（FAZ）是基于该技术的最常见的测量之一。然而，它在诊所的使用受到正常对象的FAZ区域的高变化的限制，而FAZ的体积测量的计算受到Octa扫描表征的高噪音的限制。我们设计了一种算法，该算法利用EN面图像的较高信噪比，以便在单独的丛中的毛细管不重叠的情况下有效地识别3维度（3D）中的内视网膜的毛细管网络。然后通过形态学操作处理网络以识别内视网膜的边界分割内的3D FAZ。为430只眼的数据集计算了不同丛的FAZ音量和区域。然后，使用线性混合效果模型进行测量以识别三组眼睛之间的差异：健康，糖尿病，没有糖尿病视网膜病变（DR）和糖尿病患者。结果表明，不同组之间的FAZ体积差异显着差异，但不在面积测量中。这些结果表明，比平面FAZ，体积FAZ可能是一个更好的诊断探测器。我们介绍的有效方法可以允许在诊所的FAZ音量快速计算，以及提供内视网膜毛细管网络的3D分段。

视频实例分段旨在检测视频中的段和跟踪对象。电流接近将图像级分段算法扩展到时间域。然而，这导致时间上不一致的掩模。在这项工作中，我们由于性能瓶颈而导致的掩模质量。通过此激励，我们提出了一种视频实例分段方法，可以减轻由于缺失的检测而存在的问题。由于这不能简单地使用空间信息来解决，因此我们使用帧间关节来利用时间上下文。这允许我们的网络使用来自相邻帧的框预测来重新拍摄缺失的对象，从而克服丢失的检测。我们的方法通过在YouTube-Vis基准上实现35.1％的地图，显着优于先前最先进的算法。此外，我们的方法完全在线，不需要未来的框架。我们的代码在https://github.com/anirudh-chakravarthy/objprop上公开提供。

Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the potential to reduce the invasiveness of many medical procedures. However, inserting these needles with curved trajectories increases the risk of tissue damage due to perpendicular forces exerted on the surrounding tissue by the needle's shaft, potentially resulting in lateral shearing through tissue. Such forces can cause significant damage to surrounding tissue, negatively affecting patient outcomes. In this work, we derive a tissue and needle force model based on a Cosserat string formulation, which describes the normal forces and frictional forces along the shaft as a function of the planned needle path, friction model and parameters, and tip piercing force. We propose this new force model and associated cost function as a safer and more clinically relevant metric than those currently used in motion planning for steerable needles. We fit and validate our model through physical needle robot experiments in a gel phantom. We use this force model to define a bottleneck cost function for motion planning and evaluate it against the commonly used path-length cost function in hundreds of randomly generated 3-D environments. Plans generated with our force-based cost show a 62% reduction in the peak modeled tissue force with only a 0.07% increase in length on average compared to using the path-length cost in planning. Additionally, we demonstrate the ability to plan motions with our force-based cost function in a lung tumor biopsy scenario from a segmented computed tomography (CT) scan. By planning motions for the needle that aim to minimize the modeled needle-to-tissue force explicitly, our method plans needle paths that may reduce the risk of significant tissue damage while still reaching desired targets in the body.