Using CT Images from the MICCAI 2015 — Head and Neck Auto Segmentation challenge

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Radiotherapy is now a common approach to treat cancers in the Head-and-Neck (HaN) region. To ensure that healthy organs are exposed to a minimal amount of tumor-killing radiation, it is required to segment them. Automated segmentation of such organs is required since there exists inter-annotator (i.e. between radiation oncologists) variations…


Leveraging the tf.function decorator to reduce time and memory requirements in custom tensorflow loops.

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This post is the second in a series on writing efficient training code in Tensorflow 2.x for 3D medical image segmentation. , we saw how one can extract sub-volumes from 3D CT volumes using the tf.data.Dataset API. Here, the focus is on writing custom training loops with a specific focus…


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In the last two posts, we discussed using the tf.data.Dataset API and the tf.function decorator to reduce time and memory demands. This post shall look into how to use the inbuilt profiler in Tensorflow to primarily see if our deep learning models are sitting…


Efficient extraction of medical image subvolumes for a head and neck segmentation dataset

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Loss Curves showing improved segmentation over epochs (Photo by )

Machine learning algorithms are designed so that they can “learn” from their mistakes and “update” themselves using the we provide them. But how do they quantify these mistakes? This is done via the usage of “loss functions” that help an algorithm get a sense of how erroneous its…


As a student or researcher in the field of radiation medicine, have you ever received a medical dataset from the PACS system of a hospital and wondered how should I go about understanding all those DICOM files? …


An understanding of open data sets for urban semantic segmentation shall help one understand how to proceed while training models for self-driving cars.

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Myriad efforts have been made over the last 10 years in algorithmic improvements and dataset creation for semantic segmentation tasks. Of late, there have been rapid gains in this field, a subset of visual scene understanding, due mainly to contributions by deep learning methodologies. But deep…

Prerak Mody

I'm a PhD Candidate at Leiden University Medical Centre. My research focuses on using deep learning for contour propagation of Organs at Risk in CT images.

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