Esophagus segmentation is a challenging problem due to low contrast in cT images. This paper demonstrates the use of two Fully Convolutional Network in a hierarchical workflow to improve segmentation results of Esophagus segmentation.

Introduction


This paper introduces the concept of ‘cardinality,’ an additional dimension to depth and width of a CNN, and shows that aggregating residual blocks with the same topology and hyper parameters is more effective in gaining accuracy than going deeper or wider.

Introduction


Introduction


This paper discusses a fully automated workflow for male and pelvic CT image segmentation using deep learning.

Introduction

Methods (Architecture)

Automation workflow

Kyuhee Jo

Deep medicine Enthusiast! Studying CS & Molecular and Cellular Biology at the Johns Hopkins University (kjo3@jhu.edu)

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