Welcome to TissUUmaps

Projects

We develop computational methods to combine spatially resolved information on tissue morphology with in situ RNA sequencing and protein detection. Using Deep Learning we can distinguish e.g. normal tissue from tumor tissue, and large-scale alignment of serial whole-slide images allows us to combine information from multiple protein detection methods. We also develop tools for efficient visualization and data interaction, at multiple scales. In the long-term perspective, we believe TissUUmaps will enable better diagnostics, prognostics, and treatment.

Examples displaying external data

Here we showcase examples of the use of the TissUUmaps interface displaying different kinds of data such as spage2vec for unsupervised detection of spatial gene expression constellations, and an example of prostate cancer WSI visualization showing the results of AI based grading.

Tutorials

We have created a series of tutorials you can visit: tissuumaps.research.it.uu.se/howto.html . Levels beginner to advanced, from using the demos to setting up your own copy to setting up a server with TissUUmaps in it. More tutorials coming continuosly

Publications

Towards automatic protein co-expression quantification in immunohistochemical TMA slides

Leslie Solorzano, Carla Pereira, Diana Martins, Raquel Almeida, Fatima Carneiro, Gabriela Almeida, Carla Oliveira, Carolina Wählby

IEEE Journal of Biomedical and Health Informatics
doi: doi.org/ 10.1109/JBHI.2020.3008821

Transcriptome-Supervised Classification of Tissue Morphology Using Deep Learning

Axel Andersson, Gabriele Partel, Leslie Solorzano, Carolina Wählby

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
doi: doi.org/10.1109/ISBI45749.2020.9098361

Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images

Håkan Wieslander, Philip J Harrison, Gabriel Skogberg, Sonya Jackson, Marcus Friden, Johan Karlsson, Ola Spjuth, Carolina Wählby

IEEE Journal of Biomedical and Health Informatics
doi: doi.org/10.1109/JBHI.2020.2996300

TissUUmaps: Interactive visualization of large-scale spatial gene expression and tissue morphology data

Leslie Solorzano, Gabriele Partel, Carolina Wählby

Bioinformatics - OUP
doi: doi.org/10.1093/bioinformatics/btaa541

Identification of spatial compartments in tissue from in situ sequencing data

Gabriele Partel, Markus M. Hilscher, Giorgia Milli, Leslie Solorzano, Anna H. Klemm, Mats Nilsson, Carolina Wählby

BioRxiv preprint
doi: 10.1101/765842

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

P. Ström, K. Kartasalo et al., Pathologist-level Grading of Prostate Biopsies using Artificial Intelligence,

The Lancet Oncology
doi: 10.1016/S1470-2045(19)30738-7

Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Peter Ström , Kimmo Kartasalo , Henrik Olsson , Leslie Solorzano , Brett Delahunt , Daniel M. Berney , David G. Bostwick , Andrew J. Evans , David J. Grignon , Peter A. Humphrey , Kenneth A. Iczkowski , James G. Kench , Glen Kristiansen , Theodorus H. van der Kwast , Katia R.M. Leite , Jesse K. McKenney , Jon Oxley , Chin-Chen Pan , Hemamali Samaratunga , John R. Srigley , Hiroyuki Takahashi , Toyonori Tsuzuki , Murali Varma , Ming Zhou , Johan Lindberg , Cecilia Bergström , Pekka Ruusuvuori , Carolina Wählby , Henrik Grönberg , Mattias Rantalainen , Lars Egevad , Martin Eklund

First on ArXiv: Tue, 2 Jul 2019 13:52:02 UTC
arXiv:1907.01368

Deep Learning in Image Cytometry: A Review

Anindya Gupta, Philip J. Harrison, Håkan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Ida‐Maria Sintorn, Carolina Wählby

Cytometry part A
doi: 10.1002/cyto.a.23701

Whole Slide Image Registration for the Study of Tumor Heterogeneity

Leslie Solorzano, Gabriela M. Almeida, Bárbara Mesquita, Diana Martins, Carla Oliveira, Carolina Wählby

Computational Pathology and Ophthalmic Medical Image Analysis. OMIA 2018, COMPAY 2018. Lecture Notes in Computer Science, vol 11039. 2018
doi: 10.1007/978-3-030-00949-6_12

A short feature vector for image matching: The Log-Polar Magnitude feature descriptor

Damian J. Matuszewski, Anders Hast, Carolina Wählby, Ida-Maria Sintorn

PLOS ONE 12/11 2018
doi: 10.1371/journal.pone.0188496

Improving recall of In situ sequencing by self-learned features and classical image analysis techniques

MSc Thesis by Giorgia Milli. Rel. Elisa Ficarra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2018

https://webthesis.biblio.polito.it/8002/

Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

Sajith Kecheril Sadanandan, Petter Ranefall, Sylvie Le Guyader, Carolina Wählby

Scientific Reports 2017
doi: 10.1038/s41598-017-07599-6

Decoding Gene Expression in 2D and 3D

Maxime Bombrun, Petter Ranefall, Joakim Lindblad, Amin Allalou, Gabriele Partel, Leslie Solorzano, Xiaoyan Qian, Mats Nilsson, Carolina Wählby

Lecture Notes in Computer Science (LNCS) 2017
doi: 10.1007/978-3-319-59129-2_22

Active members

Carolina Wahlby

Principal investigator

Christophe Avenel

SciLifeLab - Bioimage Informatics Facility

Gabriele Partel

PhD student

Leslie Solorzano

PhD student

Axel Andersson

PhD student

Andrea Behanova

PhD student

Eduard Chelebian

PhD student

Collaborators

Mats Nilsson

Markus Hilscher

Xiaoyan Qian

Kimmo Kartasalo

Pekka Ruusuvuori

Mattias Rantalainen

Peter Ström

Henrik Olsson

Martin Eklund

Carla Oliveira

Gabriela M. Almeida

Diana Martins

Bárbara Mesquita

Alumni

Thu Tran

Petter Ranefall

Joakim Lindblad

Maxime Bombrun

Giorgia Milli

Funding

The project is funded by the European Research Council, through a consolidator grant to Carolina Wählby.

TissUUmaps is a project within the Wählby lab at the Dept. of IT, Uppsala University

About

TissUUmaps is a project within the Wählby lab at the Dept. of IT, Uppsala University