公开(公告)号：US20230177645A1

公开(公告)日：2023-06-08

申请号：US18104812

申请日：2023-02-02

Applicant: Xidian University

Inventor： Xueli CHEN ,  Huan KANG ,  Hui XIE ,  Duofang CHEN ,  Shenghan REN ,  Wangting ZHOU

IPC: G06T3/40 ,  G06T7/00 ,  H04N25/76 ,  H04N23/955 ,  H04N23/56 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70 ,  G06V10/26 ,  G06V10/82 ,  G06V10/762 ,  G06V10/74 ,  G02B21/36 ,  G02B21/26

CPC classification number: G06T3/4053 ,  G06T7/0012 ,  H04N25/76 ,  H04N23/955 ,  H04N23/56 ,  G06T3/4007 ,  G06T3/4046 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70 ,  G06V10/26 ,  G06V10/82 ,  G06V10/763 ,  G06V10/761 ,  G02B21/365 ,  G02B21/26 ,  G06T2207/10056 ,  G06T2207/20132 ,  G06T2207/30024 ,  G06T2207/20081 ,  G06T2207/20192 ,  G06T2207/20084 ,  G06V2201/03

Abstract: A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results. Imaging visual field of the pathological section analyzer is hundreds of times that of the traditional optical microscope, a deep learning network is adopted to analyze pathological conditions of unstained pathological sections, so that the analysis process of pathological sections is simplified.