TPSN is a deformation-based model that yields a deformation map through a UNet, which takes the medical image and a template mask as inputs. The main idea is to deform a template mask describing the prescribed topology by a diffeomorphism to segment the object in the image. The topology of the shape in the template mask is well preserved under the diffeomorphic map. The diffeomorphic property of the map is controlled by introducing a regularization term related to the Jacobian in the loss function.

Information is not generally distributed uniformly in an image domain. Thus, to make the convolutional neural network focus more on those important, some deformation on convolution windows or feature maps should be applied. Besides, the topology of an image should be preserved from the ideas for defining the convolution operation.

We build a deep neural network based on quasi-conformal theories, called QC-net, to obtain diffeomorphic registration maps between corresponding data. QC-net take the landmarks in the to-be-registered images as input and output the registration mapping between them.

We introduce the first path-oriented drone trajectory planning algorithm, which performs continuous (i.e., dense) image acquisition along an aerial path and explicitly factors path quality into an optimization along with scene reconstruction quality.