Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering

Efficient Semiautomatic Segmentation of 3D Objects in Medical Images

Andrea Schenk, Guido Prause, and Heinz-Otto Peitgen, · 2000

Earlier work this paper cites.

“Advanced segmentation techniques for lung nodules, liver metastases, and enlarged lymph nodes in ct scans,”

J. H. Moltz, L. Bornemann, J. M. Kuhnigk, V. Dicken, E. Peitgen, S. Meier, H. Bolte, M. Fabel, H. C. Bauknecht, M. Hittinger, A. Kießling, M. Pusken, and H. O. Peitgen, · 2009

Earlier work this paper cites.

“Object-based analysis of ct images for automatic detection and segmentation of hypodense liver lesions,”

Michael Schwier, Jan Hendrik Moltz, and Heinz-Otto Peitgen, · 2011

Earlier work this paper cites.

U-Net: Convolutional Networks for Biomedical Image Segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015

Cited alongside, same era.

Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

Patrick Ferdinand Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Marco Armbruster, Felix Hofmann, Melvin D’Anastasi, Wieland H. Sommer, Seyed-Ahmad Ahmadi, and Bjoern H. Menze, · 2016

Cited alongside, same era.

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger, · 2016

Later among the works it cites.

“Understanding the effective receptive field in deep convolutional neural networks,”

Original

Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel, · 2016

Later among the works it cites.