Dataset Information

Structured crowdsourcing enables convolutional segmentation of histology images.

ABSTRACT: MOTIVATION:While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS:We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION:Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Amgad M

PROVIDER: S-EPMC6748796 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Structured crowdsourcing enables convolutional segmentation of histology images.

Amgad Mohamed M Elfandy Habiba H Hussein Hagar H Atteya Lamees A LA Elsebaie Mai A T MAT Abo Elnasr Lamia S LS Sakr Rokia A RA Salem Hazem S E HSE Ismail Ahmed F AF Saad Anas M AM Ahmed Joumana J Elsebaie Maha A T MAT Rahman Mustafijur M Ruhban Inas A IA Elgazar Nada M NM Alagha Yahya Y Osman Mohamed H MH Alhusseiny Ahmed M AM Khalaf Mariam M MM Younes Abo-Alela F AF Abdulkarim Ali A Younes Duaa M DM Gadallah Ahmed M AM Elkashash Ahmad M AM Fala Salma Y SY Zaki Basma M BM Beezley Jonathan J Chittajallu Deepak R DR Manthey David D Gutman David A DA Cooper Lee A D LAD

Bioinformatics (Oxford, England) 20190901 18

<h4>Motivation</h4>While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.<h4>Results</h4>We recruited 25 participants, ranging in experience from senior pathologists to medical stu ...[more]

PMID: 30726865

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Project description:BackgroundAutomated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures.ResultsResults indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture.ConclusionsWe found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community ( https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation ).

Dataset Information

Structured crowdsourcing enables convolutional segmentation of histology images.

Publications

Structured crowdsourcing enables convolutional segmentation of histology images.

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