Evolutionary Computing Application to Biomedical Image Segmentation

我们的目标是开发一个进化计算平台的原型，它接受as

Our objective is the development of a prototype of an evolutionary computing platform, which accepts as input a random sample of images of a certain type, along with their corresponding ground truths. It would output a computer program that is capable of segmenting unseen images of the same type, with a high degree of segmentation accuracy and computational efficiency. The platform itself could be described as a genetic programming method that evolves a population of feed-forward networks of nodes, corresponding to candidate image segmentation programs. The particular form of representation is inspired by Cartesian Genetic Programming (CGP). Each node is defined by its identification number (ID), image transformation, transformation parameters and the IDs of the nodes that provide its input(s). The transformation comes from an open library of transformations, which include well-known image pre-processing, segmentation and post-processing methods. The fitness of an individual will be measured from both a pixel-based and a region-based perspective. The overall evolutionary cycle searches the space of all (small-sized) feed-forward networks, of different structures and parameterizations, for those networks with highest relative fitness values. The ultimate value of our work lies in its attempt to emulate and exceed the manner in which highly-paid professionals develop distributed programs for image segmentation. Hence, we would consider our effort a success only whence a fully-functional and demonstrable prototype is produced.