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Segmentation of Lung Adenocarcinoma Cells’ Pathological Image Based on Deep Learning Method

Published: 04 June 2021 Publication History

Abstract

In the field of pathological image analysis, after obtaining the lung tissue section, the pathologist needs to estimate the proportion of the suspected cancerous area in the total cell area. The pathologist judges the positive of the lung adenocarcinoma according to the range of the ratio. A more accurate diagnosis requires sequencing the cancer cells and judging whether the gene has been mutated. And sequencing requires precise locations of the suspected lung adenocarcinoma cancer area to sample. This article proposes a framework that can classify the image and locate the cancer cell area by segmenting the tissue image. This segmentation method mainly based on 3D U-Net, and we compare the performance difference among a series of segmentation algorithms. The data in this article comes from OrigiMed Inc.

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ICIGP '21: Proceedings of the 2021 4th International Conference on Image and Graphics Processing
January 2021
231 pages
ISBN:9781450389105
DOI:10.1145/3447587
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2021

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Author Tags

  1. 3-D U-net
  2. Pathological section analysis
  3. Semantic Segmentation

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