a little bit of everything

Category: Image Analysis

Posters in Image Analysis for Microscopy

Through my PhD I had the wonderful opportunity of traveling to different countries to learn and to show my work. It was a very exciting time and I put all my skills in design to the test. In this post I want to share with you the posters I did and talk a little bit about them.

I do all my posters and figures in Inkscape, Blender and GIMP, all free and open source software for design that have all the same tools as private paid software. I also program a lot of the SVG figures using javascript and D3,

Exploratory analysis and visualization of in-situ sequencing data

TissUUmaps a tool to explore millions of points of in-situ sequencing data directly on top of the tissue. Offline or online, with documentation, you can use the browser console to analyze using javascript or simply use the GUI. Presented in EMBL – Seeing is believing as a lightning talk and in Janelia’s Women in Computational Biology conference in the U.S.

Learning from the interaction of gene expression, protein expression and tissue morphology, to make better decision about cancer treatment

In the group where I worked we are very interested in combining different sources of information and use spatial information to analyze and visualize biological phenomena. Presented at a Deep Learning workshop at the Center for Systems Biology in Dresden.

Quality Assurance and Local Regions for Whole Slide Image Registration

When trying to align WSI it is hard to evaluate and also to find out relevant locations. This poster shows how I approach this problem and gives some fun facts about WSI. I presented it in the European Congress of Digital Pathology.

Memories of the thesis defense day

The day is May 12th 2021. Location: The magnificent Universitetshuset or main university building at Uppsala University, a place where Nobel price winners and nominees gather to have dinner and discuss.

Universitetshuset – Image by University Press

Due to the world situation with COVID, only 8 people are allowed in the auditorium, but it’s enough to make me feel happy and supported.

The speaker stand is adorned by a beautiful golden emblem of the university and this time it was my time to speak. I feel important!

Here is a video of my presentation. If you want to read the thesis you can find it here.

Image Processing, Machine Learning and Visualization for Tissue Analysis – Pop science presentation at Sal IX, Universitetshuset, Uppsala University

The whole event lasted some 5 hours, but I can only share publicly my part of the presentation.

I received very good and detailed questions and debate about my work which is always encouraging so I am very thankful to the opponent and the committee.

After a closed door deliberation, committee member, Prof. Anna Kreshuk announced that they were pleased with my answers and that they agreed I should be granted my title of Teknologie doktor (Doctor of Philosophy in English).

My supervisor Carolina Wählby had many surprises for me! Along with my boyfriend and friends and colleagues I received much love, many gifts and flowers and we enjoyed a fun moment playing a quiz about me! It was really funny and had many tricky questions!

Summer started the very same day and in the end a few of us had a corona-safe picnic right in front of Universitetshuset. It was a perfect day! Quite the fairy tale finish!

One of the gifts included a driving lesson gift card, so I guess that now I really have to learn. So many adventures to come! I look forward to whatever comes my way!

Invitation to thesis defense

On May 12th 2021, 13:00 Uppsala time, I will be defending my thesis. Four and half years of hard work summarized that day. I hope to get interesting questions and debate.


Link to opponent and committee discussion. Password protected. For Opponent and committee members only!
https://uu-se.zoom.us/j/62405671377

Want to say hello or wish me good luck? Leave a comment and it will be a guest book ūüôā

Thesis document

You can find the document here. I presented all my papers and thesis in a previous post here.

What to expect?

  • This kind of event can last many hours. Plan for it, grab coffee and dinner.
  • We start at 13:00 in Uppsala time, for other times in the world see here.
  • I will give a 20 minute popular science presentation, followed by a talk by the opponent which is much longer and then questions and debate with the committee.
  • Please mute your microphones and cameras. They will not be allowed anyways. After the presentation and defense, while the committee deliberates we can all stick around and chat.
  • In the end there might be the possibility for the audience to ask questions. Please use the “raise hand” option in zoom if you want to ask a question and please be respectful.
  • If you have any questions regarding¬†this zoom event write a private message on the zoom chat to the event manager: Johan √Ėfverstedt.
  • After deliberation we will all gather in the same original zoom link
  • I am thankful for all the words of love and encouragement from friends. But please refrain from using the chat to “say hello”. We will all be able to talk and communicate while the committee deliberates. Or you can leave a comment in this post.

Who is the committee?

We are glad and thankful to count with the presence of:

Opponent:

  • Alexandru Telea

Committee:

  • Bjoern Mentze
  • Anna Kreschuk
  • Caroline Gallant
  • Stefan Seipel
  • Patric Ljung

Supervisor

Carolina Wählby

Chair

Ingela Nyström

This is me

Wish me luck!

Sign the guestbook below :

Image Processing, Machine Learning and Visualization for Tissue Analysis

Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Science and Technology 2025

Main document

Image Processing, Machine Learning and Visualization for Tissue Analysis

See the version in Uppsala University’s DiVA portal

Accompanying papers

These are the papers where I am first author. They are published under an open access license with the exception of paper III.

Paper I

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

At: Bioinformatics – Oxford University Press

Paper II

Towards Automatic Protein Co-Expression Quantification in Immunohistochemical TMA Slides

At: IEEE – Journal of Biomedical and Health Informatics

Paper III

Whole Slide Image Registration for the Study of Tumor Heterogeneity

At: MICCAI 2018 – Digital Pathology workshop

Paper IV

Machine learning for cell classification and neighborhood analysis in glioma tissue. (Under review)

At: BioRxiv preprint please note that this paper is being revised by us now and has improved greatly. We will make it available as soon as it is finished. you can find the revised version now.

Additional papers

These are publications where I also took part on as a supporting role.

Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

At: BMC Biology

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

At: The Lancet – Oncology

Deep learning in image cytometry: a review

At: Cytometry part A

Transcriptome-Supervised Classification of Tissue Morphology UsingDeep Learning

At: IEEE International Symposium on Biomedical Imaging 2020

Decoding Gene Expression in 2D and 3D

At: Scandinavian Conference on Image Analysis 2017

You can see a full list of publications in my Google Scholar profile

My role models

I believe in equality, I believe everyone brings something to the table and that working together we can do much more. More and more people agree, but for my voice, and women’s voice to be listened to, so that we are all treated equal, many have fought difficult battles.

Many women fought so that I could have a better future as a woman.

As I grew up, I have surrounded myself with more and more women who have studied and work hard, really hard and are an inspiration for me. My best mentors have been my supervisors, my professors, in different parts of the world, and I want to talk to you about them today.

Carolina Wählby
PhD supervisor
Sweden
Marcela Hernandez
Master supervisor
Colombia
Claudia Jimenez
Professor in Big Data and Data Mining, Colombia

arolina W√§hlby is my PhD supervisor in Uppsala University and she has been the most amazing guide and tutor I have met. She has devoted her precious time to guide me and many more in the past and currently to become integral and honest researchers and she’s in the forefront of life sciences, she is helping the world understand the mechanisms of disease right in the RNA the smallest unit of us. She has the most impressive curriculum! She’s a member of the Royal Academy of Sciences in Sweden, and is a professor of many courses, has been awarded numerous grants and has seen many a PhD student start and then spread their wings and fly. She’s a member of so many comittees and groups that take important decisions. I can’t possibly name them all, that is why she has her own website and media. Not only Carolina works super hard in academia, she a wife and a mother of 3 and a scout mom. I honestly do not know how she manages. She’s my hero.

arcela Hernadez is my master supervisor. She gave me the chance to go to France and study in Lyon, where she herself did her master and went through very tough times. She’s a professor in image analysis in Andes University in Colombia and she’s a leader not only in computer science but also a champion of equal opportunities. Thanks to her I was able to fulfill my dreams and she keeps working and has worked very hard so that other women and other students also have better opportunities. She’s a marvelous example of work/life balance and I think I could never do what she does, and in Colombia on top of all, where there is a really long way to go to improve women’s inclusion in all fields and decisions.

laudia Jimenez is a professor in Big Data and is the coordinator of the Master of Information Engineering (MINE in spanish) in Andes University in Colombia. She was also a huge inspiration for me and I learned so much in her course that it made me really excited (I love to learn). In her course I really learned more deeply, formal methods for data analysis and for creating systems that can handle Big Data. During one lecture she said: “Those courses of Stanford and all U.S universities have nothing on us” and chuckled. But you know what? She was right. We’re always looked down upon, we’re the little guy, we’re women working in engineering in a country that loves to push women down to the middle ages. And yet she built herself a full master program in a prestigious university to give me and other women the opportunity to learn the bleeding edge technology that allows the internet to understand data. She included me in several interesting projects during my master and for that I am very grateful.

Sometimes I think I didn’t have enough mentors when I was young and I become sad. But later in life I managed to surround myself with great women, professors and classmates and all of them are all over the world participating in great things! and I want to mention them here.

I love you all, professors, sisters, mothers, friends. Even if some might not remember me, I remember them and what they taught me.

NataŇ°a Sladoje
Professor in Image Analysis,
Uppsala University
Ingela Nyström
Professor in visualization and image analysis in medicine
Uppsala University
Gunilla Borgefors
Professor Emeritus, Digital geoemtry and image analysis
Jenny Marcela Sanchez Torres
Professor in systems theory,
Universidad Nacional de Colombia
Saida Bouakaz
Professor in image analysis,
Université Claude Bernard de Lyon 1
Ida-Maria Sintorn
Senior lecturer in image analysis Uppsala University
All my colleagues of the computer science master in Colombia. They are now working all over the world in fantastic projects. Not only working but also being cool and creative and simply awesome.

There are so many important women in my life, my friends, my family and people I admire. From many universities, many countries, of all sizes and races and ages and all such a beautiful diverse group. I feel so happy to have met you all and I feel all your support, and I want you to know that you have my support.

And perhaps above all others, the woman who changed my life completely and who I love unconditionally and who loves me, my aunt Esperanza, who has taught me the beauties and wonders of the world before I went on my own to explore them.

Christmas 2010, Seattle, WA

Registration of WSI and TMA

Our paper was accepted at IEEE journal of Biomedical Health and Informatics. I personally learned a lot and while the reviews were tough they were much appreciated.

With people focusing so much on learning methods and forgetting the classical methods which are really the base for knowledge, I’d like to talk a little bit about the paper and the one of a kind registration method developed in the MIDA group in Sweden.

Basics

If you don’t want to read the basics skip to next section

Images of a single sample can be taken in different modalities, or the same modality but at different times and conditions. Multiple views of a sample can contribute to additional information and they need to be brought to the same spatial frame of reference. The process of aligning images is called image registration.

The transformation can have various degrees of freedom. The simplest one is called rigid, when it only requires translation, rotation and isotropic scaling (same scaling in all dimensions), such transformations preserve distances within the image and preserve parallel lines. When more distortion is required such as shear, the transformation is called affine, it preserves parallel lines but does not necessarily preserve distances. When the deformation goes in different directions and magnitudes along the image the transformation is called deformable/elastic/non-rigid.

Image registration is expressed an optimization problem that is solved by iteratively searching for the parameters of a transformation that transforms an image (moving) into the reference space of image (fixed). The best alignment is decided based on a distance measure between the reference image and the moving image. Registration can then be defined as:

Image registration can be feature based or intensity based. Feature based means that several matching points have to be found in the images and then a transformation that is able to minimize the distance between these points. Intensity based methods use the pixel intensities to guide the registration. There are a few kinds that include both features and intensities, such as Alpha AMD which is used in our paper to find affine transformations between cores in a TMA.

Types of transformations

Different kinds of transformations.
Do not use figure without my permission

In order to find the co-expression between two proteins coming from two different consecutives slides I had to register the cores. To do this I used Alpha AMD which is able to use both intensity and spatial information to find the best possible affine transformation between the cores.

Why not deformable you ask? well deformable has a considerable higher number of parameters, it has less control and since the two slides are actually two different pieces of tissue they should not necessarily match perfectly or we would face the same problem as 3D tissue reconstruction, the bananna effect. Additionally, affine has the benefit of overlooking big folds or rips.

How does Alpha AMD work?

If you don’t care about the explanation and want to see the parameters for aligning tissue skip to the next section.

Alpha AMD quantizes the image and gradually aligns the cumulative sum of each level, this is on of the nifty tricks to combine spatial and intensity information in one go. It also does this in levels, in a pyramidal scheme.

Let’s see a toy example to understand how it works and what parameters to choose.

Imagine we have these two images to register. Notice that they are grayscale and have a gradient.

The levels in these gradients can be quantized in as many levels as we want, let’s see how 5 of them look in this gif showing the histogram of intensities.

Pixel intensities seen as a heightmap. Quantized level, histogram section in level.

Then using different levels in a resolution pyramid and each the cumulative sum of each quantization level we are basically using all the following information:

Parameters for aligning tissue

Since I had images coming from different slides, I used the unmixed H stains and DAB stains to convert the core to a grayscale version that did not have differences in intensities and just shows me if a pixel has tissue or not.

Then taking those grayscale representations of the core I use Alpha AMD to find the affine matrix that I can use to align the DAB images and like that find the coexpression. The video abstract in the explains further.

To get the results depicted below my parameters for Alpha AMD are:

alpha_levels=7
plevels=[128,64,32,16,8,4]
sigmas=[60,30,15.0,8.0,4.0,2.0]
symmetric_measure = False
squared_measure = False
param_iterations = 200
param_sampling_fraction = 0.4

My images are around 10,000 x 10,000 pixels wide

Unmixing then using H to find the transformation T
Final overlapping DAB for each protein

Want to know more? contact me or invite me to coffee.

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