Abstract: A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentatio

Abstract: Provided herein is a laser processing system integrated with an MBE device, including an MBE growth chamber, a sample table, an optical path mechanism, a heat insulation mechanism, and a cooling mechanism. An opening is formed in a side of the MBE growth chamber. The sample table is fixed in the MBE growth chamber, corresponds to a position of the opening, and is used for placing a substrate sample material. The optical path mechanism is relatively arranged on a side of the MBE growth chamber, and the optical path mechanism is provided with a light-emitting end. A side of the light-emitting end penetrates through the opening of the MBE growth chamber, extends into the MBE growth chamber, and is spaced apart from the sample table. The optical path mechanism is sealedly connected to the opening of the MBE growth chamber.

Abstract: A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentatio