Manipulation of tissue with surgical tools often results in large
deformations that current methods in tracking and reconstructing algorithms
have not effectively addressed. A major source of tracking errors during large
deformations stems from wrong data association between observed sensor
measurements with previously tracked scene. To mitigate this issue, we present
a surgical perception framework, SuPerPM, that leverages learning-based
non-rigid point cloud matching for data association, thus accommodating larger
deformations. The learning models typically require training data with ground
truth point cloud correspondences, which is challenging or even impractical to
collect in surgical environments. Thus, for tuning the learning model, we
gather endoscopic data of soft tissue being manipulated by a surgical robot and
then establish correspondences between point clouds at different time points to
serve as ground truth. This was achieved by employing a position-based dynamics
(PBD) simulation to ensure that the correspondences adhered to physical
constraints. The proposed framework is demonstrated on several challenging
surgical datasets that are characterized by large deformations, achieving
superior performance over state-of-the-art surgical scene tracking algorithms.

通过非刚性点云匹配的学习，解决了手术工具操作引起的大变形时跟踪误差的问题，提出了一种手术感知框架（SuPerPM），该框架利用学习模型进行数据关联，适应更大的变形，并在多个具有大变形特征的手术数据集上展示了优越的性能，超越了现有的手术场景跟踪算法。

SuPerPM: 基于深度点匹配的大变形稳健外科感知框架，从物理约束仿真数据学习

SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based  on Deep Point Matching Learned from Physical Constrained Simulation Data

Finding a realistic deformation that transforms one image into another, in
case large deformations are required, is considered a key challenge in medical
image analysis. Having a proper image registration approach to achieve this
could unleash a number of applications requiring information to be transferred
between images. Clinical adoption is currently hampered by many existing
methods requiring extensive configuration effort before each use, or not being
able to (realistically) capture large deformations. A recent multi-objective
approach that uses the Multi-Objective Real-Valued Gene-pool Optimal Mixing
Evolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformation
model has shown promise, exposing the trade-offs inherent to image registration
problems and modeling large deformations in 2D. This work builds on this
promise and introduces MOREA: the first evolutionary algorithm-based
multi-objective approach to deformable registration of 3D images capable of
tackling large deformations. MOREA includes a 3D biomechanical mesh model for
physical plausibility and is fully GPU-accelerated. We compare MOREA to two
state-of-the-art approaches on abdominal CT scans of 4 cervical cancer
patients, with the latter two approaches configured for the best results per
patient. Without requiring per-patient configuration, MOREA significantly
outperforms these approaches on 3 of the 4 patients that represent the most
difficult cases.

本研究提出了 MOREA，这是第一个基于进化算法的多目标方法，用于可变形医学图像配准，能够应对大变形，并且采用完全 GPU 加速的 3D 生物力学网格模型， 在 4 个人的腹部 CT 扫描中表现得更好。

MOREA: 用于三维医学图像多目标变形配准的 GPU 加速进化算法

MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images

Large deformations of organs, caused by diverse shapes and nonlinear shape
changes, pose a significant challenge for medical image registration.
Traditional registration methods need to iteratively optimize an objective
function via a specific deformation model along with meticulous parameter
tuning, but which have limited capabilities in registering images with large
deformations. While deep learning-based methods can learn the complex mapping
from input images to their respective deformation field, it is regression-based
and is prone to be stuck at local minima, particularly when large deformations
are involved. To this end, we present Stochastic Planner-Actor-Critic (SPAC), a
novel reinforcement learning-based framework that performs step-wise
registration. The key notion is warping a moving image successively by each
time step to finally align to a fixed image. Considering that it is challenging
to handle high dimensional continuous action and state spaces in the
conventional reinforcement learning (RL) framework, we introduce a new concept
`Plan' to the standard Actor-Critic model, which is of low dimension and can
facilitate the actor to generate a tractable high dimensional action. The
entire framework is based on unsupervised training and operates in an
end-to-end manner. We evaluate our method on several 2D and 3D medical image
datasets, some of which contain large deformations. Our empirical results
highlight that our work achieves consistent, significant gains and outperforms
state-of-the-art methods.

该论文提出了一种基于强化学习方法的医学图像配准框架，引入了新的概念 Plan 来简化高维连续空间的训练过程，并在多个 2D 和 3D 医学影像数据集上进行了测试，结果表明该方法在注册大形变图像上表现优异且超过了同类算法。