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Mahbod A, Dorffner G, Ellinger I, Woitek R, Hatamikia S. Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization. Comput Struct Biotechnol J 2024; 23:669-678. [PMID: 38292472 PMCID: PMC10825317 DOI: 10.1016/j.csbj.2023.12.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/26/2023] [Accepted: 12/26/2023] [Indexed: 02/01/2024] Open
Abstract
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.
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Affiliation(s)
- Amirreza Mahbod
- Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
| | - Georg Dorffner
- Institute of Artificial Intelligence, Medical University of Vienna, Vienna, Austria
| | - Isabella Ellinger
- Institute for Pathophysiology and Allergy Research, Medical University of Vienna, Vienna, Austria
| | - Ramona Woitek
- Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
| | - Sepideh Hatamikia
- Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
- Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria
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Franchet C, Schwob R, Bataillon G, Syrykh C, Péricart S, Frenois FX, Penault-Llorca F, Lacroix-Triki M, Arnould L, Lemonnier J, Alliot JM, Filleron T, Brousset P. Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images. Comput Biol Med 2024; 171:108130. [PMID: 38387381 DOI: 10.1016/j.compbiomed.2024.108130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024]
Abstract
Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However, a frequent drawback of AI models is their propension to make decisions based rather on bias in training dataset than on concrete biological features, thus weakening pathologists' trust in these tools. Technically, it is well known that microscopic images are altered by tissue processing and staining procedures, being one of the main sources of bias in machine learning for digital pathology. So as to deal with it, many teams have written about color normalization and augmentation methods. However, only a few of them have monitored their effects on bias reduction and model generalizability. In our study, two methods for stain augmentation (AugmentHE) and fast normalization (HEnorm) have been created and their effect on bias reduction has been monitored. Actually, they have also been compared to previously described strategies. To that end, a multicenter dataset created for breast cancer histological grading has been used. Thanks to it, classification models have been trained in a single center before assessing its performance in other centers images. This setting led to extensively monitor bias reduction while providing accurate insight of both augmentation and normalization methods. AugmentHE provided an 81% increase in color dispersion compared to geometric augmentations only. In addition, every classification model that involved AugmentHE presented a significant increase in the area under receiving operator characteristic curve (AUC) over the widely used RGB shift. More precisely, AugmentHE-based models showed at least 0.14 AUC increase over RGB shift-based models. Regarding normalization, HEnorm appeared to be up to 78x faster than conventional methods. It also provided satisfying results in terms of bias reduction. Altogether, our pipeline composed of AugmentHE and HEnorm improved AUC on biased data by up to 21.7% compared to usual augmentations. Conventional normalization methods coupled with AugmentHE yielded similar results while being much slower. In conclusion, we have validated an open-source tool that can be used in any deep learning-based digital pathology project on H&E whole slide images (WSI) that efficiently reduces stain-induced bias and later on might help increase pathologists' confidence when using AI-based products.
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Affiliation(s)
- Camille Franchet
- Institut Universitaire du Cancer-Oncopole, Pathology Department, F-31059 Toulouse, France; Université Toulouse III-Paul Sabatier, F-31000 Toulouse, France.
| | - Robin Schwob
- Thales Services Numériques, Augmented data department, F-31670 Labège, France
| | - Guillaume Bataillon
- Institut Universitaire du Cancer-Oncopole, Pathology Department, F-31059 Toulouse, France
| | - Charlotte Syrykh
- Institut Universitaire du Cancer-Oncopole, Pathology Department, F-31059 Toulouse, France
| | - Sarah Péricart
- Institut Universitaire du Cancer-Oncopole, Pathology Department, F-31059 Toulouse, France
| | | | - Frédérique Penault-Llorca
- Centre Jean Perrin, Université Clermont Auvergne, INSERM, U1240 Imagerie Moléculaire et Stratégies Théranostiques, F-63000 Clermont Ferrand, France
| | - Magali Lacroix-Triki
- Département de Pathologie, Gustave-Roussy Cancer Campus, F-94805 Villejuif, France
| | - Laurent Arnould
- Department of Pathology and Tumor Biology, Centre Georges François Leclerc, Dijon, France
| | | | - Jean-Marc Alliot
- Institut de Recherche en Informatique de Toulouse, Toulouse University, Toulouse, France; CHU Toulouse, Toulouse, France
| | - Thomas Filleron
- Institut Universitaire du Cancer-Oncopole, Institut Claudius Regaud, Biostatistics & Health Data Science Unit, F-31059 Toulouse, France
| | - Pierre Brousset
- Institut Universitaire du Cancer-Oncopole, Pathology Department, F-31059 Toulouse, France; Université Toulouse III-Paul Sabatier, F-31000 Toulouse, France; Laboratoire d'Excellence Toulouse Cancer-TOUCAN, UMR1037 CRCT, F-31037 Toulouse, France.
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