Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial on ArXiv

Super-resolution by diffusion models: low-resolution images of microtubules (left) are transformed to high-resolution (right) by diffusion model. Dataset courtesy: BioSR Dataset. (Image by H. Bachimamchi.)
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
Harshith Bachimanchi, Giovanni Volpe
arXiv: 2409.16488

Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models (DDPMs) from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions. We provide the theoretical background, mathematical derivations, and a detailed Python code implementation using PyTorch, along with techniques to enhance model performance.

Harshith Bachimanchi won best early-career researcher presentation award at AIM 2024, La Ràpita, Spain

Committee and winners for the IOP award at AIM24. From left to right: Susan Cox, Wylie Ahmed, Celia Rowland (IOP), Harshith Bachimanchi, Blanca Zufiria Gerboles, Mirja Granfors, Carlotta Viana, Gajendra Pratap Singh, Giorgio Volpe. (Photo by G. Volpe)
Harshith Bachimanchi won the best early career researcher presentation award at AIM 2024 meeting (Artificial Intelligence for iMaging 2024) held in La Ràpita, Spain, from 26 May – 1 June 2024.

The award, consisting of a certificate, and a cash prize of 500 €, is sponsored by Journal of Physics: Photonics (JPhys Photonics) from IOP Publishing.

Harshith received the prize for his presentation on “Bringing microplankton to focus: Holography and deep learning”, where he demonstrated that the combination of holographic microscopy and deep learning can be used to follow the marine microorganisms throughout their lifespan, continuously measuring their three-dimensional positions and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended periods of time. He exemplified this by showing detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

Award Certificate. (Image by H. Bachimanchi)

 

 

Harshith Bachimanchi receives the prize. (Photo by A. Callegari)

Deep-learning-powered data analysis in plankton ecology published in Limnology and Oceanography Letters

Segmentation of two plankton species using deep learning (N. scintillans in blue, D. tertiolecta in green). (Image by H. Bachimanchi.)
Deep-learning-powered data analysis in plankton ecology
Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert, Pierre De Wit, Jonathan Havenhand, Alexandra Kinnby, Daniel Midtvedt, Erik Selander, Giovanni Volpe
Limnology and Oceanography Letters (2024)
doi: 10.1002/lol2.10392
arXiv: 2309.08500

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

Presentation by H. Bachimanchi at International Forum for Computer vision in Ecology and Evolution, Lund University, 21 September 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)

Bringing microplankton to focus: Holography and deep learning
Harshith Bachimanchi
21 September 2023, 11:15 AM CEST

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

The recorded presentation can be found here:

Bubble-propelled micromotors for ammonia generation published in Nanoscale

Bubble-propelled micromotors tracked by deep learning. (Image by H. Bachimanchi.)
Bubble-propelled micromotors for ammonia generation
Rebeca Ferrer Campos, Harshith Bachimanchi, Giovanni Volpe, Katherine Villa
Nanoscale (2023)
doi: 10.1039/D3NR03804A

Micromotors have emerged as promising tools for environmental remediation, thanks to their ability to autonomously navigate and perform specific tasks at the microscale. In this study, we present the development of MnO2 tubular micromotors modified with laccase for enhanced oxidation of organic pollutants by providing an additional oxidative catalytic pathway for pollutant removal. These modified micromotors exhibit efficient ammonia generation through the catalytic decomposition of urea, suggesting their potential application in the field of green energy generation. Compared to bare micromotors, the MnO2 micromotors modified with laccase exhibit a 20% increase in rhodamine B degradation. Moreover, the generation of ammonia increased from 2 to 31 ppm in only 15 min, evidencing their high catalytic activity. To enable precise tracking of the micromotors and measurement of their speed, a deep-learning-based tracking system was developed. Overall, this work expands the potential applicability of bio-catalytic tubular micromotors in the energy field.

Harshith Bachimanchi won best early career researcher presentation award at ETAI 2023, San Diego

The three award winners. From left to right: Mite Mijalkov, Harshith Bachimanchi, Marie Drouhin. (Photo by G. Volpe.)
Harshith Bachimanchi won the best early career researcher presentation (gold) award at Emerging Topics in Artificial Intelligence (ETAI) 2023 held in San Diego, California, USA, from 20 – 24 August 2023. The award, consisting of an invitation to a part of a perspective article of AI in neurosciences, is offered by the organisers of the conference, and SPIE Optics + Photonics.

In this work, Harshith presented his recent work on combining holographic microscopy and deep learning to study the marine microplankton. He demonstrated that the combination of holographic microscopy and deep learning can be used to follow the marine microorganisms throughout their lifespan, continuously measuring their three-dimensional positions and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended periods of time. This enables to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. He exemplified this by showing detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

Award certificate of Harshith Bachimanchi. (Provided by H. Bachimanchi.)
Harshith Bachimanchi receives the award from Joana B. Pereira. (Photo by G. Volpe.)
The three award winners. From left to right: Mite Mijalkov, Harshith Bachimanchi, Marie Drouhin. (Photo by G. Volpe.)

Presentation by H. Bachimanchi at SPIE-ETAI, San Diego, 23 August 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Decoding microplankton life through holographic microscopy and deep learning
Harshith Bachimanchi
23 August 2023, 8:45 AM PDT

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

Presentation by H. Bachimanchi at XVII International Congress of the Spanish Biophysical Society, Castelldefels, 30 June 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Bringing microplankton into focus: Deep learning meets holographic microscopy
Harshith Bachimanchi
30 June 2023, 12:40 CEST

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

Presentation by H. Bachimanchi at AI for Scientific Data Analysis, Gothenburg, 1 June 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Quantitative microplankton tracking by holographic microscopy and deep learning
Harshith Bachimanchi
1 June 2023, 15:00 CEST

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

Presentation by H. Bachimanchi at Signals in the Sea mini-symposium, Lund University, 12 May 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Deep learning in plankton ecology
Harshith Bachimanchi
Presentation at Biology department, Lund University
12 May 2023, 13:00 CEST

In this mini-symposium organised by Dr. Erik Selander at Lund University, I have spoken about our recent work with marine microplankton, where we have combined holographic microscopy with deep learning to measure the ‘dry’ mass and 3D swimming dynamics of different species of planktons. The article related to this presentation can be found at the following here: Microplankton life histories revealed by holographic microscopy and deep learning.

The presentation was followed by discussions with Prof. Karin Rengefors group at Lund university on the topic of application of AI based methods for various kinds of studies in phytoplankton ecology and evolution.