sss

_pages/about.md

@@ -8,7 +8,7 @@ profile:
   align: right
   image: prof_pic_color.png
   image_circular: true # crops the image to make it circular
-  more_info: 
+  more_info:
 
 news: true # includes a list of news items
 lastest_posts: false
@@ -20,4 +20,4 @@ Marius Staring is a professor of Machine Learning for Medical Imaging, and vice
 
 He and his team develop generic machine learning approaches for automated image analysis, and apply these in the clinical and life-sciences. He has worked on machine learning for disease classification and staging, for segmentation, image registration and uncertainty estimation. In collaboration with the Department of Radiotherapy and others he explores the area of adaptive radiation therapy (photon as well as proton, CT as well as MRI). Together with the MR physics group and Philips he has developed image reconstruction techniques using deep learning, currently available in Philips MRI scanners. Image registration is a focus point in his research, developing fast and robust methods that render this technique usable in a time-critical intra-operative setting.
 
-Marius Staring is an Associate Editor of IEEE TMI, and a member of program committees of MICCAI, IEEE ISBI, SPIE MI and WBIR. He is a VENI laureate, and teaches students Technical Medicine on image processing. Open sourcing his methods has been a common theme in his career, exemplified by the image registration package [elastix](https://elastix.dev).
+Marius Staring is an Associate Editor of IEEE TMI and Medical Image Analysis, and a member of program committees of MICCAI, IEEE ISBI, SPIE MI and WBIR. He is a VENI laureate, and teaches students Technical Medicine on image processing. Open sourcing his methods has been a common theme in his career, exemplified by the image registration package [elastix](https://elastix.dev).