Gland segmentation in colon histology images: The glas challenge contest

نویسندگان

  • Korsuk Sirinukunwattana
  • Josien P. W. Pluim
  • Hao Chen
  • Xiaojuan Qi
  • Pheng-Ann Heng
  • Yun Bo Guo
  • Li Yang Wang
  • Bogdan J. Matuszewski
  • Elia Bruni
  • Urko Sanchez
  • Anton Böhm
  • Olaf Ronneberger
  • Bassem Ben Cheikh
  • Daniel Racoceanu
  • Philipp Kainz
  • Michael Pfeiffer
  • Martin Urschler
  • David R. J. Snead
  • Nasir M. Rajpoot
چکیده

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal...

متن کامل

Gland Instance Segmentation by Deep Multichannel Neural Networks

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here w...

متن کامل

Gland Instance Segmentation by Deep Multichannel Side Supervision

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here w...

متن کامل

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malig...

متن کامل

A Hybrid 3D Colon Segmentation Method Using Modified Geometric Deformable Models

Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis.  Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Medical image analysis

دوره 35  شماره 

صفحات  -

تاریخ انتشار 2017