





About usAll About MICLab
Research
Learn more about our researches on MRI and human brain image processing through advanced algoritms.
Publications
Find here a comprehensive list of MICLab´s latest published research and keep updated with all our developments.
Tools
MICLab´s researchers are developing innovative tools to help scientists analyze computer images. Find them here.
Team
Click here for more information on our main researchers, graduate and undergraduate students and their lines of work.
NewsLatest from MICLab
We are currently looking for motivated Postdoctoral Fellows to join our group and take part in our research project on COVID-19. More details here.
We’ve just published a paper in Neuroimage, “A benchmark for hypothalamus segmentation on T1-weighted MR images”. Together with the paper, we’ve launched a challenge in codalab (https://lnkd.in/
EventsEvents Calendar
- 6 June 2023 – Prof. Rittner will be given a Talk at the ISMRM 2023 (https://www.ismrm.org/23m/) Educational Session – I got the grant, now what?!
- 15 July 2023 – SIPAIM 2023 (https://sipaim.org/) in Mexico. Call for papers coming soon. Stay tunned!
- 10 Nov 2023 – Deadline submission for ISBI 2024 (https://biomedicalimaging.
org/2024/)
NewsMICLab News
First place in MRS Reconstruction Challenge at ISBI 2023!
I couldn't be more proud of my students Gabriel Dias, Mateus Oliveira, that together with Paula Dornhofer Paro Costa and...
MICLab wins the Best Paper Award (Master degree student) at the BRAINN Congress for the third year in a row
This time the award for the best paper in the Master Degree category at the BRAINN Congress was given to...
MICLab at II International Workshop on Hyperspectral Image
Last week, FEA (UNICAMP) hosted the II International Workshop on Hyperspectral Imaging, chaired by Prof. Dr. Douglas Barbin (FEA), comprehending...
MICLab wins prize at the The 17th International Symposium on Medical Information Processing and Analysis
Our work on Multitasking segmentation of lung and COVID-19 findings in CT scans using modified EfficientDet, UNet and MobileNetV3 models received...