Segmenting The Kidney On CT Scans Via Crowdsourcing

Mehta, Paras; Sandfort, Veit; Gheysens, Daan; Braeckevelt, Gert-Jan; Berte, Jonathan; Summers, Ronald M.

doi:10.1109/isbi.2019.8759240

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…The STAPLE algorithm compares segmentations and computes a probabilistic estimate of the true segmentation. For narrowly focused applications such as colonic polyp classification and kidney segmentation, crowdsourcing of labels by nonexperts may be feasible (45,46). Heim et al (47) compared segmentations of the liver performed by nonexperts, engineers with domain knowledge, medical students, and radiologists.…”

Section: Ground Truth or Label Qualitymentioning

confidence: 99%

Preparing Medical Imaging Data for Machine Learning

Willemink¹,

Koszek²,

Hardell³

et al. 2020

Radiology

Self Cite

614

364

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Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

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Section: Ground Truth or Label Qualitymentioning

confidence: 99%

Preparing Medical Imaging Data for Machine Learning

Willemink¹,

Koszek²,

Hardell³

et al. 2020

Radiology

Self Cite

614

364

View full text Add to dashboard Cite

show abstract

“…In the literature, experiences with crowdsourcing medical imaging tasks to untrained persons in the community-at-large have been described with variable success. Such tasks include annotations of airways, lung nodules, kidney and liver segmentations, and colon polyp classification on CT colonography images [18][19][20][21].…”

Section: Discussionmentioning

confidence: 99%

Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase

et al. 2020

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Purpose To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance. Methods In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists' segmentations were compared against radiologists' segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland-Altman analysis. Results Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [− 2.74 cc (min − 92.96 cc, max 87.47 cc) versus − 23.57 cc (min − 77.32, max 30.19)]. Conclusion Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.

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“…Distributing the labeling task among more human labelers reduces the labeling burden on individuals but increases overall labeling work and raises consistency issues that may require averaged or consensus labels among several labelers. Recent experiments have found value in crowdsourced segmentation labels by nonexpert reviewers (10,11). For tasks with abundant imaging data, low-quality labels may be sufficient to train a network.…”

Section: Teaching Pointsmentioning

confidence: 99%

Deep Learning: An Update for Radiologists

Cheng¹,

Montagnon²,

Yamashita³

et al. 2021

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Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data. These networks have numerous applications in radiology, particularly in image classification, object detection, semantic segmentation, and instance segmentation. The authors provide an update on a recent primer on deep learning for radiologists, and they review terminology, data requirements, and recent trends in the design of CNNs; illustrate building blocks and architectures adapted to computer vision tasks, including generative architectures; and discuss training and validation, performance metrics, visualization, and future directions. Familiarity with the key concepts described will help radiologists understand advances of deep learning in medical imaging and facilitate clinical adoption of these techniques.Online supplemental material is available for this article.

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Segmenting The Kidney On CT Scans Via Crowdsourcing

Cited by 7 publications

References 9 publications

Preparing Medical Imaging Data for Machine Learning

Preparing Medical Imaging Data for Machine Learning

Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase

Deep Learning: An Update for Radiologists

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