Article
Biology
Xiaoming Liu, Zhengsheng Guo, Jun Cao, Jinshan Tang
Summary: This study introduces a new method for nucleus segmentation in pathological images using a deep neural network, which utilizes multiple short residual connections and dilated convolutions to improve accuracy. Additionally, distance map and contour information are incorporated to address the segmentation of touching nuclei.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Review
Oncology
Gregory Verghese, Jochen K. Lennerz, Danny Ruta, Wen Ng, Selvam Thavaraj, Kalliopi P. Siziopikou, Threnesan Naidoo, Swapnil Rane, Roberto Salgado, Sarah E. Pinder, Anita Grigoriadis
Summary: Computational pathology refers to the use of deep learning techniques and algorithms to analyze and interpret histopathology images. Despite its promising potential, the integration of computational pathology in clinical settings is hindered by various obstacles, including operational, technical, regulatory, ethical, financial, and cultural challenges.
JOURNAL OF PATHOLOGY
(2023)
Article
Computer Science, Information Systems
Hasnain Ali Shah, Jae-Mo Kang
Summary: In digital pathology, accurate cell nuclei segmentation is crucial for medical image analysis. This paper proposes a novel DL architecture, CBAM-Residual U-Net, to improve accuracy and robustness. It utilizes special modules to learn shallow and deep features and uses attention mechanism to focus on important features of cell nuclei for accurate segmentation.
Article
Computer Science, Artificial Intelligence
Kai Yao, Kaizhu Huang, Jie Sun, Amir Hussain
Summary: This article aims to develop a reliable and robust method for detecting, segmenting, and classifying nuclei in histopathology data. The authors address the challenges by treating the detection and classification of each nucleus as a semantic keypoint estimation problem, using dynamic instance segmentation to obtain class-agnostic masks for nuclei center points. They also propose a Joint Pyramid Fusion Module (JPFM) to enhance local features and achieve better nuclei detection and classification. The superior performance of the proposed approach is demonstrated across 19 different tissue types.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Biology
Shyam Lal, Devikalyan Das, Kumar Alabhya, Anirudh Kanfade, Aman Kumar, Jyoti Kini
Summary: This study presents NucleiSegNet, a deep learning network architecture for nuclei segmentation in H&E stained liver cancer histopathology images. The architecture includes robust residual block, bottleneck block, and attention decoder block, and outperforms state-of-the-art methods in nuclei segmentation tasks. Additionally, a new liver dataset (KMC liver dataset) was introduced for further research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Medicine, General & Internal
Amirreza Mahbod, Gerald Schaefer, Georg Dorffner, Sepideh Hatamikia, Rupert Ecker, Isabella Ellinger
Summary: In this paper, a CNN-based dual decoder U-Net model is proposed for nuclei instance segmentation in histological images. The model predicts foreground and distance maps and utilizes a watershed algorithm for multi-nuclei segmentation. Additionally, an independent U-Net model is developed for nuclei classification.
FRONTIERS IN MEDICINE
(2022)
Article
Engineering, Biomedical
G. Murtaza Dogar, Muhammad Shahzad, Muhammad Moazam Fraz
Summary: Nuclei instance segmentation and classification in histology plays a major role in pathology image examination, but the high variability in nuclei characteristics and degraded image quality pose challenges. To address these problems, a novel deep learning model leveraging distance information among nuclei instances is proposed, achieving finer segmentation and accurate classification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Editorial Material
Computer Science, Interdisciplinary Applications
Adrien Foucart, Olivier Debeir, Christine Decaestecker
Summary: The MoNuSAC 2020 challenge hosted at the ISBI 2020 conference has been analyzed, revealing three problems in the computation of the metric used for ranking. The incorrect code version was used to rank the algorithms in the challenge. The results can be replicated using the code provided on GitHub.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Biology
Iqra Kiran, Basit Raza, Areesha Ijaz, Muazzam A. Khan
Summary: Cancer is the second deadliest disease globally, and early detection is crucial. This study proposes a model for nuclei segmentation, combining U-Net and DenseRes-Unet to effectively extract features. The distance map and binary threshold techniques enhance the interior and contour information of nuclei in the images. Evaluation on publicly available datasets shows that the proposed model achieves high accuracy and performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Loay Hassan, Adel Saleh, Mohamed Abdel-Nasser, Osama A. Omer, Domenec Puig
Summary: This paper presents deep semantic nuclei segmentation models for multi-institutional whole-slide imaging images of different organs, addressing challenges such as color variation, overlapping nuclei, and ambiguous boundaries. A feasible deep learning nuclei segmentation model is proposed by combining robust architectures, showing efficacy compared to existing methods.
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
(2021)
Article
Biotechnology & Applied Microbiology
Nicola Altini, Antonio Brunetti, Emilia Puro, Maria Giovanna Taccogna, Concetta Saponaro, Francesco Alfredo Zito, Simona De Summa, Vitoantonio Bevilacqua
Summary: The study presents an innovative method based on gradient-weighted class activation mapping (Grad-CAM) saliency maps for nuclei segmentation in biomedical image analysis. The proposed method demonstrates superior performance in isolating different nuclei instances, with the ability to be generalized for various organs and tissues.
BIOENGINEERING-BASEL
(2022)
Article
Urology & Nephrology
Nassim Bouteldja, Barbara M. Klinkhammer, Roman D. Buelow, Patrick Droste, Simon W. Otten, Saskia Freifrau von Stillfried, Julia Moellmann, Susan M. Sheehan, Ron Korstanje, Sylvia Menzel, Peter Bankhead, Matthias Mietsch, Charis Drummer, Michael Lehrke, Rafael Kramann, Juergen Floege, Peter Boor, Dorit Merhof
Summary: This study utilized a convolutional neural network for accurate segmentation of kidney tissue in various species and disease models, showing high performance and providing a new high-throughput tool for pathology analysis.
JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY
(2021)
Article
Engineering, Biomedical
Amit Kumar Chanchal, Shyam Lal, Jyoti Kini
Summary: The study introduces a deep learning framework for histopathology image segmentation, utilizing a high-resolution encoder path, an atrous spatial pyramid pooling bottleneck module, and a powerful decoder. Experimental results show that the proposed method outperforms benchmark segmentation models on three histopathology datasets.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2021)
Article
Biology
A. Ben Hamida, M. Devanne, J. Weber, C. Truntzer, V Derangere, F. Ghiringhelli, G. Forestier, C. Wemmert
Summary: Digital pathology plays a major role in the diagnosis and prognosis of tumors. Deep Learning methods show promise for tissue classification and segmentation in histopathological images. This study focuses on using DL architectures to classify and highlight colon cancer regions in sparsely annotated data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biology
Shajahan Aboobacker, Deepu Vijayasenan, Sumam David, Pooja K. Suresh, Saraswathy Sreeram
Summary: This study aims to predict the malignancy in effusion cytology images and reduce scanning time using deep learning models. The authors extend two semi-supervised learning models and introduce reverse augmentation to address spatial alterations in image annotation. The results show that the extended models improve the accuracy of malignancy pixel prediction and successfully save scanning time compared to the baseline model.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Multidisciplinary Sciences
Adham Safieddine, Emeline Coleno, Soha Salloum, Arthur Imbert, Abdel-Meneem Traboulsi, Oh Sung Kwon, Frederic Lionneton, Virginie Georget, Marie-Cecile Robert, Thierry Gostan, Charles-Henri Lecellier, Racha Chouaib, Xavier Pichon, Herve Le Hir, Kazem Zibara, Florian Mueller, Thomas Walter, Marion Peter, Edouard Bertrand
Summary: Using high-throughput single molecule FISH screening, researchers found that 8 human mRNAs localize to centrosomes with unique cell cycle dependent patterns using an active polysome targeting mechanism.
NATURE COMMUNICATIONS
(2021)
Editorial Material
Respiratory System
Sandra Curras-Alonso, Juliette Soulier, Thomas Walter, Florian Mueller, Arturo Londono-Vallejo, Charles Fouillade
EUROPEAN RESPIRATORY JOURNAL
(2021)
Editorial Material
Biochemistry & Molecular Biology
Emma Lundberg, Jan Funke, Chris Bakal, Virginie Uhlmann, Daniel Gerlich, Thomas Walter, Ann Carpenter, Luis Pedro Coehlo
Article
Biochemistry & Molecular Biology
Xavier Pichon, Konstadinos Moissoglu, Emeline Coleno, Tianhong Wang, Arthur Imbert, Marie-Cecile Robert, Marion Peter, Racha Chouaib, Thomas Walter, Florian Mueller, Kazem Zibara, Edouard Bertrand, Stavroula Mili
Summary: RNA localization and local translation are crucial for various cellular functions. In mammals, KIF1C motor plays a dual role in transporting RNAs and clustering them within cytoplasmic protrusions. Notably, KIF1C is also able to transport its own mRNA, indicating a possible feedback loop at the level of mRNA transport.
Article
Microscopy
Mael Balluet, Florian Sizaire, Youssef El Habouz, Thomas Walter, Jeremy Pont, Baptiste Giroux, Otmane Bouchareb, Marc Tramier, Jacques Pecreaux
Summary: Artificial intelligence is now being used in optical microscopy for cell detection and classification in post-acquisition analysis. A real-time image processing method has been proposed to balance accurate detection and performance execution. By utilizing machine learning and a feature extractor, fast and discriminant features can be identified for precise cell classification.
JOURNAL OF MICROSCOPY
(2022)
Article
Biochemistry & Molecular Biology
Arthur Imbert, Wei Ouyang, Adham Safieddine, Emeline Coleno, Christophe Zimmer, Edouard Bertrand, Thomas Walter, Florian Mueller
Summary: In this paper, a modular tool called FISH-quant v2 is presented for quantitative analysis of gene expression and data exploration. The tool is applicable for large-scale smFISH image data sets and provides quantitative analysis of RNA localization patterns, visualizing variations within single cells and cell populations.
Article
Biochemical Research Methods
Adham Safieddine, Emeline Coleno, Frederic Lionneton, Abdel-Meneem Traboulsi, Soha Salloum, Charles-Henri Lecellier, Thierry Gostan, Virginie Georget, Cedric Hassen-Khodja, Arthur Imbert, Florian Mueller, Thomas Walter, Marion Peter, Edouard Bertrand
Summary: The ability to visualize RNA in its native subcellular environment has been greatly advanced by single-molecule fluorescence in situ hybridization (smFISH). However, the high cost of generating individual fluorescent probe sets has hindered systematic experiments in medium- or high-throughput formats. Here, we introduce high-throughput smFISH (HT-smFISH), a cost-efficient method for imaging hundreds to thousands of single endogenous RNA molecules. HT-smFISH uses RNA probes transcribed in vitro from a large pool of unlabeled oligonucleotides, reducing costs per targeted RNA compared to other smFISH methods.
Article
Environmental Sciences
Julia D. Sigwart, Angelika Brandt, Davide Di Franco, Elva Escobar Briones, Sarah Gerken, Andrew J. Gooday, Candace J. Grimes, Kamila Gluchowska, Sven Hoffmann, Anna Maria Jazdzewska, Elham Kamyab, Andreas Kelch, Henry Knauber, Katharina Kohlenbach, Olmo Miguez-Salas, Camille Moreau, Akito Ogawa, Angelo Poliseno, Andreu Santin Muriel, Anne Helene S. Tandberg, Franziska I. Theising, Thomas Walter, Anne-Cathrin Woelfl, Chong Chen
Summary: Abyssal plains cover a large portion of the ocean floor and were previously considered featureless, but recent research has revealed substantial biological heterogeneity. By analyzing high-definition camera images from three stations in the Bering Sea, researchers found significant variations in the density and distribution of visible epifauna, including different megafaunal taxa. The findings suggest that abyssal habitats exhibit comparable levels of biological heterogeneity to terrestrial continental realms.
FRONTIERS IN MARINE SCIENCE
(2023)
Article
Cell Biology
Tristan Lazard, Guillaume Bataillon, Peter Naylor, Tatiana Popova, Francois-Clement Bidard, Dominique Stoppa-Lyonnet, Marc-Henri Stern, Etienne Decenciere, Thomas Walter, Anne Vincent-Salomon
Summary: Homologous recombination DNA-repair deficiency (HRD) is a recognized marker in ovarian and breast cancer chemotherapies. By using digital pathology, HRD can be predicted and related phenotypes can be identified, providing new insights into the phenotypic impact of HRD.
CELL REPORTS MEDICINE
(2022)
Article
Cell Biology
Melanie Lubrano, Yaelle Bellahsen-Harrar, Sylvain Berlemont, Sarah Atallah, Emmanuelle Vaz, Thomas Walter, Cecile Badoual
Summary: Our study investigates the potential of deep learning in assisting pathologists with the automatic and reliable classification of head and neck lesions. We created a large-scale histological sample database and developed a weakly supervised model for classification based on whole-slide images. The model demonstrated high accuracy in classifying different lesion types and the confidence score allowed for accurate differentiation between reliable and uncertain predictions.
Review
Oncology
Jeppe Thagaard, Glenn Broeckx, David B. Page, Chowdhury Arif Jahangir, Sara Verbandt, Zuzana Kos, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Guray Akturk, Jonas S. Almeida, Isabel Alvarado-Cabrero, Mohamed Amgad, Farid Azmoudeh-Ardalan, Sunil Badve, Nurkhairul Bariyah Baharun, Eva Balslev, Enrique R. Bellolio, Vydehi Bheemaraju, Kim R. M. Blenman, Luciana Botinelly Mendonca Fujimoto, Najat Bouchmaa, Octavio Burgues, Alexandros Chardas, Maggie U. Cheang, Francesco Ciompi, Lee A. D. Cooper, An Coosemans, German Corredor, Anders B. Dahl, Flavio Luis Dantas Portela, Frederik Deman, Sandra Demaria, Johan Dore Hansen, Sarah N. Dudgeon, Thomas Ebstrup, Mahmoud Elghazawy, Claudio Fernandez-Martin, Stephen B. Fox, William M. Gallagher, Jennifer M. Giltnane, Sacha Gnjatic, Paula Gonzalez-Ericsson, Anita Grigoriadis, Niels Halama, Matthew G. Hanna, Aparna Harbhajanka, Steven N. Hart, Johan Hartman, Soren Hauberg, Stephen Hewitt, Akira Hida, Hugo M. Horlings, Zaheed Husain, Evangelos Hytopoulos, Sheeba Irshad, Emiel A. M. Janssen, Mohamed Kahila, Tatsuki R. Kataoka, Kosuke Kawaguchi, Durga Kharidehal, Andrey Khramtsov, Umay Kiraz, Pawan Kirtani, Liudmila L. Kodach, Konstanty Korski, Aniko Kovacs, Anne-Vibeke Laenkholm, Corinna Lang-Schwarz, Denis Larsimont, Jochen K. Lennerz, Marvin Lerousseau, Xiaoxian Li, Amy Ly, Anant Madabhushi, Sai K. Maley, Vidya Manur Narasimhamurthy, Douglas K. Marks, Elizabeth S. McDonald, Ravi Mehrotra, Stefan Michiels, Fayyaz ul Amir Afsar Minhas, Shachi Mittal, David A. Moore, Shamim Mushtaq, Hussain Nighat, Thomas Papathomas, Frederique Penault-Llorca, Rashindrie D. Perera, Christopher J. Pinard, Juan Carlos Pinto-Cardenas, Giancarlo Pruneri, Lajos Pusztai, Arman Rahman, Nasir Mahmood Rajpoot, Bernardo Leon Rapoport, Tilman T. Rau, Jorge S. Reis-Filho, Joana M. Ribeiro, David Rimm, Anne Roslind, Anne Vincent-Salomon, Manuel Salto-Tellez, Joel Saltz, Shahin Sayed, Ely Scott, Kalliopi P. Siziopikou, Christos Sotiriou, Albrecht Stenzinger, Maher A. Sughayer, Daniel Sur, Susan Fineberg, Fraser Symmans, Sunao Tanaka, Timothy Taxter, Sabine Tejpar, Jonas Teuwen, E. Aubrey Thompson, Trine Tramm, William T. Tran, Jeroen van Der Laak, Paul J. van Diest, Gregory E. Verghese, Giuseppe Viale, Michael Vieth, Noorul Wahab, Thomas Walter, Yannick Waumans, Hannah Y. Wen, Wentao Yang, Yinyin Yuan, Reena Md Zin, Sylvia Adams, John Bartlett, Sibylle Loibl, Carsten Denkert, Peter Savas, Sherene Loi, Roberto Salgado, Elisabeth Specht Stovgaard
Summary: The clinical significance of tumor-immune interaction in breast cancer has been established. Tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative and HER2-positive breast cancer. The use of machine learning (ML) to automatically evaluate TILs has shown promising results. However, there are challenges in implementing this in trial and routine clinical management, including technical slide issues, ML and image analysis aspects, data challenges, and validation issues.
JOURNAL OF PATHOLOGY
(2023)
Review
Oncology
David B. Page, Glenn Broeckx, Chowdhury Arif Jahangir, Chowdhury Jahangir, Sara Verbandt, Rajarsi R. Gupta, Jeppe Thagaard, Reena Khiroya, Zuzana Kos, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Jonas S. Almeida, Isabel Alvarado-Cabrero, Farid Azmoudeh-Ardalan, Sunil Badve, Nurkhairul Bariyah Baharun, Enrique R. Bellolio, Vydehi Bheemaraju, Kim R. M. Blenman, Luciana Botinelly Mendonca Fujimoto, Octavio Burgues, Maggie Chon U. Cheang, Francesco Ciompi, Lee A. D. Cooper, An Coosemans, German Corredor, Flavio Luis Dantas Portela, Frederik Deman, Sandra Demaria, Sarah N. Dudgeon, Mahmoud Elghazawy, Scott Ely, Claudio Fernandez-Martin, Susan Fineberg, Stephen B. Fox, William M. Gallagher, Jennifer M. Giltnane, Sacha Gnjatic, Paula Gonzalez-Ericsson, Anita Grigoriadis, Niels Halama, Matthew G. Hanna, Aparna Harbhajanka, Alexandros Hardas, Steven N. Hart, Johan Hartman, Stephen Hewitt, Akira Hida, Hugo M. Horlings, Zaheed Husain, Evangelos Hytopoulos, Sheeba Irshad, Emiel A. M. Janssen, Mohamed Kahila, Tatsuki R. Kataoka, Kosuke Kawaguchi, Durga Kharidehal, Andrey Khramtsov, Umay Kiraz, Pawan Kirtani, Liudmila L. Kodach, Konstanty Korski, Aniko Kovacs, Anne-Vibeke Laenkholm, Corinna Lang-Schwarz, Denis Larsimont, Jochen K. Lennerz, Marvin Lerousseau, Xiaoxian Li, Amy Ly, Anant Madabhushi, Sai K. Maley, Vidya Manur Narasimhamurthy, Douglas K. Marks, Elizabeth S. McDonald, Ravi Mehrotra, Stefan Michiels, Fayyaz ul Amir Afsar Minhas, Shachi Mittal, David A. Moore, Shamim Mushtaq, Hussain Nighat, Thomas Papathomas, Frederique Penault-Llorca, Rashindrie D. Perera, Christopher J. Pinard, Juan Carlos Pinto-Cardenas, Giancarlo Pruneri, Lajos Pusztai, Arman Rahman, Nasir Mahmood Rajpoot, Bernardo Leon Rapoport, Tilman T. Rau, Jorge S. Reis-Filho, Joana M. Ribeiro, David Rimm, Anne-Vincent Salomon, Manuel Salto-Tellez, Joel Saltz, Shahin Sayed, Kalliopi P. Siziopikou, Christos Sotiriou, Albrecht Stenzinger, Maher A. Sughayer, Daniel Sur, Fraser Symmans, Sunao Tanaka, Timothy Taxter, Sabine Tejpar, Jonas Teuwen, E. Aubrey Thompson, Trine Tramm, William T. Tran, Jeroen van Der Laak, Paul J. van Diest, Gregory E. Verghese, Giuseppe Viale, Michael Vieth, Noorul Wahab, Thomas Walter, Yannick Waumans, Hannah Y. Wen, Wentao Yang, Yinyin Yuan, Sylvia Adams, John Mark Seaverns Bartlett, Sibylle Loibl, Carsten Denkert, Peter Savas, Sherene Loi, Roberto Salgado, Elisabeth Specht Stovgaard, Guray Akturk, Najat Bouchmaa
Summary: Modern histologic imaging platforms combined with machine learning methods offer new opportunities for studying the spatial distribution of immune cells in the tumor microenvironment. However, there is currently no standardized method for describing or analyzing spatial immune cell data, and most previous spatial analyses have been simplistic. In this review, two approaches (raster versus vector-based) for reporting and analyzing spatial data are outlined, along with a summary of reported spatial immune cell metrics and their prognostic associations in various cancers. Two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, are also discussed, along with potential research opportunities to improve the clinical utility of these spatial biomarkers.
JOURNAL OF PATHOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Peter Naylor, Tristan Lazard, Guillaume Bataillon, Marick Lae, Anne Vincent-Salomon, Anne-Sophie Hamy, Fabien Reyal, Thomas Walter
Summary: The article introduces the automated analysis of stained histological sections and the use of Deep Learning for computational analysis. It proposes modifications to the standard cross-validation procedure for small cohorts and presents a new architecture for treatment prediction.
FRONTIERS IN SIGNAL PROCESSING
(2022)
Meeting Abstract
Medicine, Research & Experimental
Guillaume Bataillon, Anne Vincent-Salomon, Thomas Walter, Marc-Henri Stern, Peter Naylor, Youlia Kirova, Tristan Lazard, Etienne Decenciere, Francois Clement Bidard, Dominique Stoppa-Lyonnet
LABORATORY INVESTIGATION
(2021)
Meeting Abstract
Pathology
Guillaume Bataillon, Anne Vincent-Salomon, Thomas Walter, Marc-Henri Stern, Peter Naylor, Youlia Kirova, Tristan Lazard, Etienne Decenciere, Francois Clement Bidard, Dominique Stoppa-Lyonnet