π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hello Reader,
Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β
Lately I was working on a task for 3D medical imaging where I needed to load specific slices from a series of DICOM files.
So if a patient case has 300 slices, how would you load slices 50 to 100 only?
In this quick tutorial I will show you 2 code examples on how to do this in Python.
Approach 1: Using PyDicom
Approach 1: Using SimpleITK
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When working with DICOMs for medical imaging, from time to time youβll find cases where the image is completely dark. Why do we have such cases? And how to tackle them using Python?
The darkness or brightness of DICOM (Digital Imaging and Communications in Medicine) images, such as MRIs and CT scans, can depend on several factors:
If youβre building a deep learning model for a medical imaging application, you might want to adjust the brightness for dark patient cases.
To do this, you have several options such as: windowing, histogram equalization and adaptive histogram equalization.
Hereβs how to apply them in Python:
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Here's a sample code on how to do this:
βDeep Learning for Object Detection using Tensorflow.
βDeep Learning for Image Segmentation using Mask RCNN and Tensorflow.
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βThat's it for this week's edition, I hope you enjoyed it!
by Nour Islam Mokhtari from pycad.co
π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hi Reader,, Welcome to the PYCAD newsletter, where every week you receive doses of machine learning and computer vision techniques and tools to help you learn how to build AI solutions to empower the most vulnerable members of our society, patients. TotalSegmentator : Whole Body Segmentation at your Fingertips This free tool available online can do full body segmentation, it's called TotalSegmentator. I have already mentioned this tool in a previous edition of the newsletter, but in this...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β A Medical Imaging Expert Told Me This Recently I saw a post on LinkedIn where a medical imaging expert showcased his work of segmenting the lungs and its bronchial trees. You can...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β How we helped accelerate inference time for a client's AI product Below is a screenshot of a benchmark we did for a client of ours. The goal was to accelerate inference time. This...