An active learning approach for the interactive and guided segmentation of tomography data

被引：2

作者：

Kazimi, Bashir ^{[1
]}

Heuser, Philipp ^{[2
]}

Schluenzen, Frank

Cwieka, Hanna

Krueger, Diana

Zeller-Plumhoff, Berit ^{[1
]}

Wieland, Florian ^{[1
]}

Hammel, Joerg U. ^{[1
]}

Beckmann, Felix ^{[1
]}

Moosmann, Julian ^{[1
]}

机构：

[1] Helmholtz Zentrum Hereon, Max Planck Str 1, D-21502 Geesthacht, Germany

[2] Deutsch Elektronen Synchrotron DESY, Notkestr 85, D-22607 Hamburg, Germany

来源：

DEVELOPMENTS IN X-RAY TOMOGRAPHY XIV | 2022年 / 12242卷

关键词：

tomography; synchrotron radiation; deep learning; active learning; segmentation;

D O I：

10.1117/12.2637973

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

The Helmholtz-Zentrum Hereon is operating several tomography end stations at the beamlines P05 and P07 of the synchrotron radiation facility PETRA III at DESY in Hamburg, Germany. Attenuation and phase contrast imaging techniques are provided as well as sample environments for in situ/operando/vivo experiments for applications in biology, medicine, materials science, etc. Very large and diverse data sets with varying spatiotemporal resolution, noise levels and artifacts are acquired which are challenging to process and analyze. Here we report on an active learning approach for the semantic segmentation of tomography data using a guided and interactive framework, and evaluate different acquistion functions for the selection of images to be annotated in the iterative process.

引用

页数：6

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