This short abstract describes a solution to the COSAS 2024 competition on Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation from histopathological image patches. The main challenge in the task of segmenting this type of cancer is a noticeable domain shift encountered when changing acquisition devices (microscopes) and also when tissue comes from different organs. The two tasks proposed in COSAS were to train on a dataset of images from three different organs, and then predict segmentations on data from unseen organs (dataset T1), and to train on a dataset of images acquired on three different scanners and then segment images acquired with another unseen microscope. We attempted to bridge the domain shift gap by experimenting with three different strategies: standard training for each dataset, pretraining on dataset T1 and then fine-tuning on dataset T2 (and vice-versa, a strategy we call \textit{Cross-Task Pretraining}), and training on the combination of dataset A and B. Our experiments showed that Cross-Task Pre-training is a more promising approach to domain generalization.
Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation
Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Flensburg University of Applied Sciences（弗伦斯堡应用技术大学）；Julius-Maximilians-Universität Würzburg（维尔zburg尤利乌斯-马克西米利安大学）
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models
Chongqing University of Technology（重庆理工大学）；Chongqing Zhijian Life Technology Co. LTD（重庆智健生命科技有限公司）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
本文提出使用 Rein 微调方法高效微调 ConvNeXt 和 DINOv2 等视觉基础模型，以解决跨器官和跨扫描仪腺癌分割中的领域差异问题，并在 COSAS2024 挑战赛中取得优异分数。
Biomedical Image Analysis Group, Imperial College London（帝国理工学院生物医学图像分析组）；Comprehensive Cancer Imaging Centre, Hammersmith Hospital, Imperial College London（帝国理工学院哈默史密斯医院综合癌症成像中心）；The Royal Marsden NHS Foundation Trust, London（伦敦皇家马斯登国民保健服务基金会信托）
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation. Despite the advancement of machine learning in automatic segmentation, performance often degrades when algorithms are applied on new data acquired from different scanners or sequences than the training data. Manual annotation is costly and time consuming if it has to be carried out for every new target domain. In this work, we investigate automatic selection of suitable subjects to be annotated for supervised domain adaptation using the concept of reverse classification accuracy (RCA…
Sun Yat-sen University（中山大学）；University of Dundee（邓迪大学）；Technical University of Munich（慕尼黑工业大学）
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then in…
Department of Science and Technology, Linkoping University（林雪平大学科技学院）；Sectra AB（Sectra公司）；Center for Medical Image Science and Visualization, Linkoping University（林雪平大学医学影像科学与可视化中心）
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications. The problem is notably prominent for the task of metastasis detection in lymph nodes, due to the tissue's low tumor-to-non-tumor ratio, resulting in labor- and time-intensive annotation processes for the pathologists. This work explores alternatives on how to augment the training data for colon carcinoma metastasis detection when there is limited or no representation of the target domain. Through an exhaustive study of cross-validated experiments wit…
University of Warwick（华威大学）；Eindhoven University of Technology（埃因霍温理工大学）；The Chinese University of Hong Kong（香港中文大学）；University of Central Lancashire（中央兰开夏大学）；ExB Research and Development（ExB研发中心）；University of Freiburg（弗莱堡大学）；BIOSS Centre for Biological Signalling Studies, University of Freiburg（弗莱堡大学生物信号研究中心）；Google-DeepMind（谷歌DeepMind）；Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Biomedical Imaging Laboratory (LIB)（索邦大学，巴黎第六大学，国家科学研究中心，国家健康与医学研究院，生物医学成像实验室）
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to …
Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of …
Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）；Guilin University of Electronic Technology（桂林电子科技大学）；Guangdong Provicial People’s Hospital Ganzhou Hospital（广东省人民医院赣州医院）
Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outs…
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；The University of Melbourne（墨尔本大学）
An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a \emph{single} domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of …
Image Sciences Institute, UMC Utrecht, Utrecht University（乌得勒支大学医学中心（UMC Utrecht）影像科学研究所）；Brain Center Rudolf Magnus, UMC Utrecht, Utrecht University（乌得勒支大学医学中心（UMC Utrecht）鲁道夫·马格努斯脑中心）；Department of Radiology, UMC Utrecht, the Netherlands（乌得勒支大学医学中心放射科（荷兰））；Department of Radiology, LUMC, Leiden, the Netherlands（莱顿大学医学中心（LUMC）放射科（荷兰））；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系（瑞士奥尔施维尔））；Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, AB, Canada（卡尔加里大学（加拿大阿尔伯塔省）霍奇基斯脑研究所放射学与临床神经科学部）；NLP Logix；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；School of Biomedical Engineering and Im（生物医学工程与...学院（原文未完整显示））
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge …
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains). Our method is inspired by the fact that the spatial relationship between internal structures in medical images is relatively fixed, e.g., a spleen is always located at the tail of a pancreas, which serves as a latent variable to transfer the knowledge shared across multiple domains. We formulate the spatial relationship by solving a jigsaw p…
Gottfried Schatz Research Center: Biophysics（戈特弗里德·沙茨研究中心：生物物理部）；Institute of Computer Graphics and Vision（计算机图形与视觉研究所）；Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨技术大学）
Even though many semantic segmentation methods exist that are able to perform well on many medical datasets, often, they are not designed for direct use in clinical practice. The two main concerns are generalization to unseen data with a different visual appearance, e.g., images acquired using a different scanner, and efficiency in terms of computation time and required Graphics Processing Unit (GPU) memory. In this work, we employ a multi-organ segmentation model based on the SpatialConfiguration-Net (SCN), which integrates prior knowledge of the spatial configuration among the labelled organ…
Centre for Machine Intelligence and Data Science, IIT Bombay, India（机器智能与数据科学中心，IIT博因达，印度）；Department of Electrical Engineering, IIT Bombay, India（电气工程系，IIT博因达，印度）
针对跨扫描仪、染色与采集域偏移下的非典型有丝分裂分类，本文提出风格扰动、弱标签特征对齐与 EMA 教师蒸馏相结合的训练方案，在 MIDOG 2025 初步榜单取得均衡高性能。
Ultrasound images are one of the most widely used techniques in clinical settings to analyze and detect different organs for study or diagnoses of diseases. The dependence on subjective opinions of experts such as radiologists calls for an automatic recognition and detection system that can provide an objective analysis. Previous work done on this topic is limited and can be classified by the organ of interest. Hybrid neural networks, linear and logistic regression models, 3D reconstructed models, and various machine learning techniques have been used to solve complex problems such as detectio…
In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-regist…
Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机学院）；Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University（首都医科大学北京友谊医院核医学科）；Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health（首都医科大学北京儿童医院放射科）
Siemens Healthineers（西门子医疗）；Vanderbilt University（范德堡大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the views in texts, and record clinically relevant organ measurements. Hence, automatic view classification and landmark detection of the organs can be instrumental to streamline the examination workflow. However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e.g., low contrast and large variations, but also the heterogeneity across tasks, i.e., one classification task…
PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（新型一代人工智能技术及其交叉应用重点实验室（东南大学））；Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University（公共卫生学院生物统计学系，全球健康中心，南京医科大学）；Institute of High-Performance Computing, Agency for Science, Technology and Research（高性能计算研究所，科技研究局）
Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task- or imaging modality specific architectures with a decision-making process that is often hard to explain and interpret. Here, we propose a framework that embeds existing DNs into a low-dimensional subspace induced by the learnable explicit feature map (LEFM) layer. Compared to the existing DN, the framework adds one hyperparameter and onl…
Vanderbilt University（范德堡大学）；Sigma Technologies（12西格玛科技公司）；Cleveland Clinic（克利夫兰诊所）；Vanderbilt University Medical Center（范德堡大学医学中心）
Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal CT). However, the effectiveness of tissue window normalization is questionable since the generalizability of the trained model might be further harmed, especially when such models are applied to new cohorts with different CT reconstruction kernels, contrast mechanisms, dynamic variations in the acquisition, and physiological changes. We evaluate the effectiveness of both with and without using soft tissue window norma…
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）
Digital pathology is one of the most significant developments in modern medicine. Pathological examinations are the gold standard of medical protocols and play a fundamental role in diagnosis. Recently, with the advent of digital scanners, tissue histopathology slides can now be digitized and stored as digital images. As a result, digitized histopathological tissues can be used in computer-aided image analysis programs and machine learning techniques. Detection and segmentation of nuclei are some of the essential steps in the diagnosis of cancers. Recently, deep learning has been used for nucl…
Medical Center University of Freiburg（弗莱堡大学医学中心）；Faculty of Medicine, University of Freiburg（弗莱堡大学医学院）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；German Oncology Center, European University（欧洲大学德国肿瘤学中心）
Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, access to large-scale datasets with annotated regions of interest is scarce, and obtaining good results is thus difficult. To overcome these challenges, we introduce IB-U-Nets, a novel architecture with inductive bias, inspired by the visual processing in vertebrates. With the 3D U-Net as the base, we add two 3D residual components to the second enc…
Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment
Healthcare Informatics, University of Illinois Springfield（医疗信息学，伊利诺伊大学斯普林菲尔德分校）
