TrainingData.io Blog

Productivity #19: Dicom radiology reports

Pdf and text documents are supported as standard radiology reports to be included in the dataset alongside radiology images. Radiology reports are prepared by radiologists to present the findings in the radiological imaging data like x-Ray, CT, MRI. Ragiologists spend years training skills to read complex radiological images. They use

productivity

Productivity #18: Annotator Performance Management

Every labeling job provides statistics for each annotator. Statistics include total number of labels for each class, number of accepted labels, number of rejected labels. Aggregated sum for all annotators is also available. StatisticsThe line charts show labeling time in seconds spent on each day, review time in seconds, number

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Productivity #17: Window Level for DICOM and Nifti

CT and MRI dicom images can be viewed using multiple window settings in order to see different features of the image. For example, there would be different window settings to look at Abdominal / soft tissues, Bone, Brain, Liver, Lung, Subdural brain. The Window Center (0028,1050), Window Width (0028,1051)

Radiology

Radiology AI: Comparison of best ML data labeling tools for dicom data

Dicom file format is the most commonly used file format for storing radiology image data. All radiology equipment like CT and MRI machines, manufactured by many companies, generate dicom images that are sent to a pacs server. Automatic analysis by various AI algorithms happens on the PAC server. The images

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Productivity #16: Fastest Video Annotation Tool

TrainingData.io provides fastest way to annotate video data. Video TimelineVideo Timeline Today we are announcing general availability of the feature Video Timeline. This feature allows annotators to play & pause the video, view annotations, propagate annotations, interpolate annotations in an interface as shown in the image below. Video TimelineVideo

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Productivity #15: Interpolation of labels in a video

TrainingData.io provides geometric interpolation of objects in its annotation tools. Draw a label as shown below Navigate to a different framePropagate label in frame #54Edit label in frame #54Interpolate in frame #54Video

Artificial Intelligence

Sensor Fusion & Interpolation for LIDAR 3D Point Cloud Data Labeling

Autonomous vehicle development has seen higher volume and greater accuracy of sensory data being recorded from hardware sensors. Sensors have gone up in number. Additionally newer generation of sensors are recording higher resolution and more accurate measurements. In this piece we will explore how sensor fusion allows greater degree of

RSNA

Meet TrainingData.io at Virtual-RSNA 2020

TrainingData.io is one-stop shop for your radiology training data requirements. We are very excited to invite you to meet us at the AI Pavilion. We will be showcasing new products at the Virtual-RSNA 2020. Please use the calendar below to book an appointment with us. 3D Segmentation of MRI

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Productivity #14: Brightness Contrast and Opacity

TrainingData.io's annotation tool has easily accessible controls to change brightness of image, contrast of image and opacity of the annotations. As shown in the image below the top bar has slider controls to change brightness, contrast and opacity. Brightnss, Contrast and OpacityThe following video shows how to modify opacity

activelearning

TrainingData.io brings Active Learning Radiology Data Pipeline with NVIDIA Clara

Machine learning model development can consume a lot of resources. Model development involves continuous back-and-forth between the labeling team and the model developement team. The labeling team and the model development team work on an active learning data pipeline. Active Learning Data PipelineActive Learning Data Pipeline (Source: State of the

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Productivity #13: RESTful API for software integration

Swagger API documentation Postman API documentation

lidar

LIDAR 3D Point Cloud Annotation Tool from TrainingData.io

We are very excited to announce the general availability of a 3D point cloud annotation tool for datasets generated by LIDARs. It is well known that the autonomous driving industry is philosophically divided into two camps. One camp believes computer vision (camera sensor) to be sufficient for building the perception

covid-19

Automatic detection of COVID-19 in chest CT using NVIDIA Clara on TrainingData.io

In March 2020, to help data scientists working on COVID-19 diagnostic tools, TrainingData.io provided a free collaborative workspace preloaded with the open-source dataset including chest X-ray and chest CT images. We would like to thank our users for their contributions. We learned a lot from our users. Now, TrainingData.

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Productivity #12: Add New Class During Annotation

When annotator is creating annotations, annotator might want to add a new class definition. Admin can allow annotator to add class under Project Settings --> Appearance --> Allow annotator to Annotator can add new class or edit existing labels by selecting + Add Class inside the annotation tool in

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Productivity #11: Import PNG Mask as pre-labels

The annotation tool enables user to import PNG masks as pre-labels. This is fastest way to implement model assisted annotation without exposing your model's intellectual property. The input dataset should contain image files and one Png mask file per label as seen in the dataset below (import-prelabel-mask.csv) Image files

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Productivity #10: Superpixel Size in Segmentation

Superpixel segmentation in the annotation tool allows different sizes of Superpixel at different zoom levels. There is a trade-off between accuracy of annotation and time to annotation as the Superpixel size changes. Large Superpixel – Zoom outFor fast semantic segmentation annotator can zoom out on the image and perform Superpixel segmentation.

video

Semi-Automatic Video Annotation @ Scale for Machine Learning

It is no secret that the amount of video data being generated by diagnostic and monitoring systems alone is very large. For machine learning teams, it is difficult to pre-process such large quanitities of training data. To enable fast annotation of large volumes of video content, TrainingData.io has launched

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Productivity #9: Dataset with multiple folders

There are use cases like radiology where a radiologist would like to images from axial view, sagital view, coronal view in a single labeling project. To create a dataset with multiple folders first create an archive (zip or tar) file that preserves the folder structure including the image file. Create

covid-19

COVID-19 Imaging Dataset for Annotation & Collaboration

At TrainingData.io we understand the urgent need for diagnostic tools to identify COVID-19. In the initial stages of this outbreak all countries, including the US, have faced one major problem - lack of diagnostic tools and proper testing. To be able to build diagnostic tools, data scientists are scrambling

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Productivity #8: Multi Planar Views for Radiology

Radiological datasets including MRI and CT-scans contain images from multiple planes like axial plane, coronal plane and sagittal plane. TraningData.io allows multi-planar views with upto four planes included in a single view as shown in the image below. The axial plane, coronal plane and sagital planes are synchronized with

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Productivity #7: Review System

The annotation tool has an annotation mode and a review mode. In review mode a reviewer can review every individual annotation. Review each individual annotationFor every annotation reviewer can accept or reject the annotation, give it a score between 0 and 10, and leave a comment for the annotator. When

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Productivity #6: Image Queuing Strategy

In large industrial scale annotation projects, there are millions of images with multiple annotators participating. Images and videos can be distributed among the annotators using different distribution strategies. All the image & video assets to be labeled are stored in a queue. There are two separate queues for labeling and

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Productivity #5: External cloud storage

TrainingData.io supports external cloud storage in form of pre-authenticated URLs. The application support S3, GCP and Azure cloud storage. Download a sample csv file populated with a list of pre-authenticated URLs. In image below the folder sample2/2782 contains dicom files. (dicom urls start with dicomweb://) Use the following

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Productivity #4: Edit a Polygon

New polygonTo make new polygon select polygon tool in left panel. Click-n-release mouse button at each point where you wish to make a vertex of the resulting polygon. Select a polygonSelect a polygon by clicking any vertex (end point) of the polygon. Edit a polygonTo edit a polygon, remove a

RSNA

TrainingData.io Adopts NVIDIA Clara to Help Data Scientists Build AI 20X Faster

To help data scientists build their AI models faster, TrainingData.io has adopted NVIDIA Clara, a healthcare domain-specific set of SDKs, libraries, tools and reference applications for AI-assisted annotation, federated learning and real-time image and video analysis. NVIDIA Clara runs on the NVIDIA EGX platform for AI computing on edge