TY  - JOUR
A1  - Wech, Tobias
A1  - Ankenbrand, Markus Johannes
A1  - Bley, Thorsten Alexander
A1  - Heidenreich, Julius Frederik
T1  - A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series
JF  - Magnetic Resonance in Medicine
N2  - Purpose
Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing.

Methods
A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architecture was pretrained for segmentation of undersampled spatiotemporal cine MRI. Transfer learning was then performed using samples from a second database, comprising 108 non-Cartesian radial cine series of the midventricular myocardium to optimize the performance for authentic data. The performance was evaluated for different levels of undersampling by the Dice similarity coefficient (DSC) with respect to reference labels, as well as by deriving ventricular volumes and myocardial masses.

Results
Without transfer learning, the pretrained model performed moderately on true radial data [maximum number of projections tested, P = 196; DSC = 0.87 (left ventricle), DSC = 0.76 (myocardium), and DSC =0.64 (right ventricle)]. After transfer learning with authentic data, the predictions achieved human level even for high undersampling rates (P = 33, DSC = 0.95, 0.87, and 0.93) without significant difference compared with segmentations derived from fully sampled data.

Conclusion
A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis.
KW  - undersampling
KW  - cardiovascular magnetic resonance (CMR)
KW  - deep learning
KW  - radial
KW  - semantic segmentation
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-257616
VL  - 87
IS  - 2
ER  - 
TY  - JOUR
A1  - Ankenbrand, Markus Johannes
A1  - Lohr, David
A1  - Schlötelburg, Wiebke
A1  - Reiter, Theresa
A1  - Wech, Tobias
A1  - Schreiber, Laura Maria
T1  - Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI
JF  - Magnetic Resonance in Medicine
N2  - Purpose
Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians.

Methods
A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning.

Results
Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICE\(_{LV}\) = 0.835 and DICE\(_{MY}\) = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICE\(_{LV}\) = 0.900 and DICE\(_{MY}\) = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICE\(_{LV}\) = 0.908, DICE\(_{MY}\) = 0.805).

Conclusions
This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.
KW  - 7T
KW  - ultrahigh-field
KW  - transfer learning
KW  - segmentation
KW  - neural networks
KW  - deep learning
KW  - cardiac magnetic resonance
KW  - cardiac function
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-257604
VL  - 86
IS  - 4
ER  - 
TY  - THES
A1  - Ankenbrand, Markus Johannes
T1  - Squeezing more information out of biological data - development and application of bioinformatic tools for ecology, evolution and genomics
T1  - Mehr aus biologischen Daten herausholen - Entwicklung und Anwendung bioinformatischer Programme für Ökologie, Evolution und Genomik
N2  - New experimental methods have drastically accelerated the pace and quantity at which biological data is generated. High-throughput DNA sequencing is one of the pivotal new technologies. It offers a number of novel applications in various fields of biology, including ecology, evolution, and genomics. However, together with those opportunities many new challenges arise. Specialized algorithms and software are required to cope with the amount of data, often requiring substantial training in bioinformatic methods. Another way to make those data accessible to non-bioinformaticians is the development of programs with intuitive user interfaces.

In my thesis I developed analyses and programs to tackle current problems with high-throughput data in biology. In the field of ecology this covers the establishment of the bioinformatic workflow for pollen DNA meta-barcoding. Furthermore, I developed an application that facilitates the analysis of ecological communities in the context of their traits. Information from multiple public databases have been aggregated and can now be mapped automatically to existing community tables for interactive inspection. In evolution the new data are used to reconstruct phylogenetic trees from multiple genes. I developed the tool bcgTree to automate this process for bacteria. Many plant genomes have been sequenced in current years. Sequencing reads of those projects also contain data from the chloroplasts. The tool chloroExtractor supports the targeted extraction and analysis of the chloroplast genome. To compare the structure of multiple genomes specialized software is required for calculation and visualization of the relationships. I developed AliTV to address this. In contrast to existing programs for this task it allows interactive adjustments of produced graphics. Thus, facilitating the discovery of biologically relevant information. Another application I developed helps to analyze transcriptomes even if no reference genome is present. This is achieved by aggregating the different pieces of information, like functional annotation and expression level, for each transcript in a web platform. Scientists can then search, filter, subset, and visualize the transcriptome.

Together the methods and tools expedite insights into biological systems that were not possible before.
N2  - Neue experimentelle Methoden haben die Geschwindigkeit und Masse, in der biologische Daten generiert werden, in den letzten Jahren enorm gesteigert. Eine zentrale neue Technologie ist die Hochdurchsatzsequenzierung von DNA. Diese Technik eröffnet eine ganze Reihe Anwendungsmöglichkeiten in vielen Bereichen der Biologie, einschließlich der Ökologie, Evolution und Genomik. Neben den neuen Möglichkeiten treten jedoch auch neue Herausforderungen auf. So bedarf es spezialisierter Algorithmen und Computerprogramme, um mit der Masse an Daten umgehen zu können. Diese erfordern in der Regel ein fundiertes Training in bioinformatischen Methoden. Ein Weg, die Daten auch Wissenschaftlern ohne diesen Hintergrund zugänglich zu machen ist die Entwicklung von Programmen, die sich intuitiv bedienen lassen.

In meiner Doktorarbeit habe ich Analysen und Programme entwickelt, um einige aktuelle Probleme mit Hochdurchsatzdaten in der Biologie zu lösen. Im Bereich der Ökologie umfasst das die Etablierung der bioinformatischen Methode, um Pollen DNA Metabarcoding durchzuführen. Darüberhinaus habe ich eine Anwendung entwickelt, die es ermöglicht Artgemeinschaften im Kontext ihrer Eigenschaften zu erforschen. Dazu wurden Informationen aus diversen öffentlichen Datenbanken zusammen getragen. Diese können nun automatisch auf bestehende Projekte übertragen und interaktiv analysiert werden. Im Bereich der Evolution ermöglichen die neuen Daten phylogenetische Berechnungen mit multiplen Genen durchzuführen. Um dies für Bakterien zu automatisieren habe ich das Programm bcgTree entwickelt. In den letzten Jahren wurden viele pflanzliche Genome sequenziert. Die Sequenzdaten des pflanzlichen Genoms enthalten auch die des Chloroplasten. Das Programm chloroExtractor unterstützt die gezielte Analyse des Chloroplasten Genoms. Um jedoch die Struktur mehrerer Genome miteinander vergleichen zu können, wird spezielle Software benötigt, die den Vergleich berechnen und visuell darstellen kann. Daher habe ich das Programm AliTV entwickelt. Im Gegensatz zu bestehenden Programmen erlaubt AliTV interaktive Anpassungen der erzeugten Grafik. Das erleichtert es die relevanten Informationen zu finden.
Ein weiteres von mir entwickeltes Programm hilft dabei Transkriptom Daten zu analysieren, auch wenn kein Referenzgenom vorliegt. Dazu werden Informationen zu jedem Transkript, z.B. Funktion und Expressionslevel, in einer Webanwendung aggregiert. Forscher können diese durchsuchen, filtern und graphisch darstellen.

Zusammen eröffnen die entwickelten Methoden und Programme die Möglichkeit, Erkenntnisse über biologische Systeme zu erlangen, die bislang nicht möglich waren.
KW  - bioinformatics
KW  - research software
KW  - ecology
KW  - evolution
KW  - genomics
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-156344
ER  -