Towards an Efficient Way of Building Annotated Medical Image Collections for Big Data Studies

Gur, Yaniv; Moradi, Mehdi; Bulu, Hakan; Guo, Yufan; Compas, Colin B.; Syeda-Mahmood, Tanveer

doi:10.1007/978-3-319-67534-3_10

Cited by 9 publications

(17 citation statements)

References 5 publications

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“…Classify (Albarqouni et al, 2016a), (Brady et al, 2014), (Brady et al, 2017), , (dos Reis et al, 2015), (Eickhoff, 2014), (Foncubierta Rodríguez and Müller, 2012), (Gur et al, 2017), (de Herrera et al, 2014), (Holst et al, 2015), (Huang and Hamarneh, 2017), (Keshavan et al, 2018), (Lawson et al, 2017), (Malpani et al, 2015), (Mavandadi et al, 2012, (Mitry et al, 2013), (Mitry et al, 2015), (Nguyen et al, 2012), (Park et al, 2016), (Park et al, 2017), (Smittenaar et al, 2018), (Sonabend et al, 2017), (Sullivan et al, 2018) Segment (Roethlingshoefer et al, 2017), (Boorboor et al, 2018), (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Chávez-Aragón et al, 2013), (Cheplygina et al, 2016), (Ganz et al, 2017), (Gurari et al, 2015b), (Heller et al, 2017), (Irshad et al, 2015), (Lee and Tufail, 2014), (Lee et al, 2016), (Lejeune et al, 2017), (Luengo-Oroz et al, 2012), (Maier-Hein et al, 2014a), (Maier-Hein et al, 2016), (O'Neil et al, 2017), (Park et al, 2018),…”

Section: Task Papersmentioning

confidence: 99%

“…Abdomen (Roethlingshoefer et al, 2017), (Heim, 2018), (Heller et al, 2017), (Maier-Hein et al, 2014a), (Maier-Hein et al, 2014b), (Maier-Hein et al, 2015), (Maier-Hein et al, 2016), (McKenna et al, 2012), (Nguyen et al, 2012), (Park et al, 2016), (Park et al, 2017), (Park et al, 2018), (Rajchl et al, 2017) Brain (Ganz et al, 2017), (Keshavan et al, 2018), (Rajchl et al, 2016), (Sonabend et al, 2017), (Timmermans et al, 2016) Eye (Brady et al, 2014), (Brady et al, 2017), (Lee and Tufail, 2014), (Lee et al, 2016), (Leifman et al, 2015), (Mitry et al, 2013), (Mitry et al, 2015), (Mitry et al, 2016) Heart (Gur et al, 2017) Histo (Albarqouni et al, 2016a), (Albarqouni et al, 2016b), (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Della Mea et al, 2014), (dos Reis et al, 2015), (Eickhoff, 2014), (Irshad et al, 2015), (Irshad et al, 2017), (Lawson et al, 2017), (Luengo-Oroz et al, 2012), (Mavandadi et al, 2012), (Sameki et al, 2016), (Sharma et al, 2017), ...…”

Section: Domain Papersmentioning

confidence: 99%

“…Click (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Della Mea et al, 2014), (Huang and Hamarneh, 2017), (Lawson et al, 2017), (Lejeune et al, 2017), (Luengo-Oroz et al, 2012), (Park et al, 2018), (Rajchl et al, 2016) Click + Compare (Maier-Hein et al, 2014b), (Maier-Hein et al, 2015), (Maier-Hein et al, 2016) Click + Draw (Irshad et al, 2015) Compare (Ørting et al, 2017) Draw (Roethlingshoefer et al, 2017), (Boorboor et al, 2018), (Chávez-Aragón et al, 2013), (Cheplygina et al, 2016), (Ganz et al, 2017), (Gurari et al, 2015b), (Heller et al, 2017), (Lee and Tufail, 2014), (Lee et al, 2016), (Maier-Hein et al, 2014a), (O'Neil et al, 2017), (Sameki et al, 2016), (Sharma et al, 2017) Rate (Albarqouni et al, 2016a), (Albarqouni et al, 2016b), (Brady et al, 2014), (Brady et al, 2017), , (dos Reis et al, 2015), (Eickhoff, 2014), (Foncubierta Rodríguez and Müller, 2012), (Gur et al, 2017), (de Herrera et al, 2014), (Holst et al, 2015), (Keshavan et al, 2018), (Mavandadi et al, 2012), (McKenna et al, 2012), (Mitry ...…”

Section: Interaction Papersmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Crowdsourcing in Medical Image Analysis

Ørting

Doyle²,

Hilten

et al. 2020

HCj

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Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

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Section: Task Papersmentioning

confidence: 99%

Section: Domain Papersmentioning

confidence: 99%

Section: Interaction Papersmentioning

confidence: 99%

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A Survey of Crowdsourcing in Medical Image Analysis

Ørting

Doyle²,

Hilten

et al. 2020

HCj

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“…However, it has been pointed out that there is a pressing need to develop methods for tagging images with semantic descriptors, e.g. for decision support or context awareness [17], [18]. For example, context-aware augmented reality (AR) in surgery is becoming a topic of interest.…”

Section: A Related Workmentioning

confidence: 99%

Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Moccia

Wirkert²,

Kenngott

et al. 2018

IEEE Trans. Biomed. Eng.

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“…To achieve generalization, deep learning models require large amounts of data that are accurately annotated. Obtaining such a dataset for a variety of medical images is challenging because expert annotation can be expensive, time consuming [19,30], and often limited by the subjective interpretation [24]. Moreover, other issues such as privacy and under-representation of rare conditions impede developing such datasets [54,44].…”

Section: Introductionmentioning

confidence: 99%

Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images

et al. 2018

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Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data. However, networks trained on synthetic data often fail to generalize to real data. Cinematic rendering simulates the propagation and interaction of light passing through tissue models reconstructed from CT data, enabling the generation of photorealistic images. In this paper, we present one of the first applications of cinematic rendering in deep learning, in which we propose to fine-tune synthetic data-driven networks using cinematically rendered CT data for the task of monocular depth estimation in endoscopy. Our experiments demonstrate that: (a) convolutional neural networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model. Our empirical evaluation demonstrates that networks fine-tuned with cinematically rendered data predict depth with 56.87% less error for rendered endoscopy images and 27.49% less error for real porcine colon endoscopy images.

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Towards an Efficient Way of Building Annotated Medical Image Collections for Big Data Studies

Cited by 9 publications

References 5 publications

A Survey of Crowdsourcing in Medical Image Analysis

A Survey of Crowdsourcing in Medical Image Analysis

Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images

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