Segmentation in radiology
Segmentation of radiological images is important in many fields. Volumetry, visualization including VR/AR, 3D printing, radiotherapy, (co-)registration, and many other post-processing tools are some examples that often rely on segmentation.
Unfortunately, segmentation is still generally regarded as laborious and uninteresting by many. However recent progress in the field of computer vision has enabled the use of AI for both automated and interactive segmentation. For radiology, we hope that these new developments will hasten broad clinical adoption of volumetry to replace the subjective ruler-based distance measurements that are still the current clinical standard.

About us and our vision
We are two radiologists based in Oslo, Norway who crave more AI-enhancement in our daily practice. It is our belief that AI-based segmentation is technologically ready for broad clinical implementation. Although MedSeg is not meant for direct clinical use, we hope that it, together with our open-access segmentation database, will facilitate faster development of clinically validated models. Radiological segmentation should be simple, fun and accessible for everyone.

We are proud that MedSeg is beginning to be used by several research projects and we ourselves find it increasingly useful for effective high quality segmentation. If you are a radiologist, we hope that you share our vision and would consider helping us with annotating more data (CT and MRI). This data is made freely available so that anyone can download and develop tools that we believe can enhance radiology in general. If you are interested, please get in touch with us. We can provide openly accessible data that can be used for this purpose.

MedSeg is based on the following openly available tools and resources:
DICOM reader: https://github.com/rii-mango/Daikon
Nifti reader: https://github.com/rii-mango/NIFTI-Reader-JS
Deep learning (DL) in browser: https://www.tensorflow.org/js
Development of DL models: https://keras.io/ + https://www.tensorflow.org/
DeepGrow module: https://arxiv.org/abs/1903.08205
Ideas and experience from a Python-based segmentation tool with AI-capabilities. RILContour: https://link.springer.com/article/10.1007/s10278-019-00232-0
Another great advanced application with many post-processing tools, 3D Slicer: https://www.slicer.org/
Overview of web-based DICOM viewers: https://medevel.com/14-best-browser-web-based-dicom-viewers-projects/