المؤلفون: Krishnaswamy, Deepa, Bontempi, Dennis, Thiriveedhi, Vamsi, Punzo, Davide, Clunie, David, Bridge, Christopher P, Aerts, Hugo JWL, Kikinis, Ron, Fedorov, Andrey

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition

الوصف: Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::391dfce1dd8f7e147585a27e6eb15119Test

المؤلفون: Gupta, Sharut, Singh, Praveer, Chang, Ken, Qu, Liangqiong, Aggarwal, Mehak, Arun, Nishanth, Vaswani, Ashwin, Raghavan, Shruti, Agarwal, Vibha, Gidwani, Mishka, Hoebel, Katharina, Patel, Jay, Lu, Charles, Bridge, Christopher P., Rubin, Daniel L., Kalpathy-Cramer, Jayashree

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)

الوصف: Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant decline in performance when tested at other institutions. While pooling datasets from multiple institutions and retraining may provide a straightforward solution, it is often infeasible and may compromise patient privacy. An alternative approach is to fine-tune the model on subsequent institutions after training on the original institution. Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting. In this paper, we develop an approach to address catastrophic forget-ting based on elastic weight consolidation combined with modulation of batch normalization statistics under two scenarios: first, for expanding the domain from one imaging system's data to another imaging system's, and second, for expanding the domain from a large multi-institutional dataset to another single institution dataset. We show that our approach outperforms several other state-of-the-art approaches and provide theoretical justification for the efficacy of batch normalization modulation. The results of this study are generally applicable to the deployment of any clinical deep learning model which requires domain expansion.
Comment: First three authors contributed equally

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea75086f0f134b25b6ebf3320e5172dbTest

المؤلفون: Biondetti, Giorgio Pietro, Gauriau, Romane, Bridge, Christopher P., Lu, Charles, Andriole, Katherine P.

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)

الوصف: As interest in applying machine learning techniques for medical images continues to grow at a rapid pace, models are starting to be developed and deployed for clinical applications. In the clinical AI model development lifecycle (described by Lu et al. [1]), a crucial phase for machine learning scientists and clinicians is the proper design and collection of the data cohort. The ability to recognize various forms of biases and distribution shifts in the dataset is critical at this step. While it remains difficult to account for all potential sources of bias, techniques can be developed to identify specific types of bias in order to mitigate their impact. In this work we analyze how the distribution of scanner manufacturers in a dataset can contribute to the overall bias of deep learning models. We evaluate convolutional neural networks (CNN) for both classification and segmentation tasks, specifically two state-of-the-art models: ResNet [2] for classification and U-Net [3] for segmentation. We demonstrate that CNNs can learn to distinguish the imaging scanner manufacturer and that this bias can substantially impact model performance for both classification and segmentation tasks. By creating an original synthesis dataset of brain data mimicking the presence of more or less subtle lesions we also show that this bias is related to the difficulty of the task. Recognition of such bias is critical to develop robust, generalizable models that will be crucial for clinical applications in real-world data distributions.
Comment: 15 pages, 4 figures. This paper has been submitted to the Journal of Digital Imaging (Springer Journal) and it is now in review

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e91c55bcce4b4bd6ddf23d39c3bae167Test

المؤلفون: Bridge, Christopher P., Ioannou, Christos, Noble, J. Alison

المصدر: Medical Image Analysis. 36:147-161

مصطلحات موضوعية: Radiological and Ultrasound Technology, Radiology Nuclear Medicine and imaging, Health Informatics, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design

الوصف: Interpretation of ultrasound videos of the fetal heart is crucial for the antenatal diagnosis of congenital heart disease (CHD). We believe that automated image analysis techniques could make an important contribution towards improving CHD detection rates. However, to our knowledge, no previous work has been done in this area. With this goal in mind, this paper presents a framework for tracking the key variables that describe the content of each frame of freehand 2D ultrasound scanning videos of the healthy fetal heart. This represents an important first step towards developing tools that can assist with CHD detection in abnormal cases. We argue that it is natural to approach this as a sequential Bayesian filtering problem, due to the strong prior model we have of the underlying anatomy, and the ambiguity of the appearance of structures in ultrasound images. We train classification and regression forests to predict the visibility, location and orientation of the fetal heart in the image, and the viewing plane label from each frame. We also develop a novel adaptation of regression forests for circular variables to deal with the prediction of cardiac phase. Using a particle-filtering-based method to combine predictions from multiple video frames, we demonstrate how to filter this information to give a temporally consistent output at real-time speeds. We present results on a challenging dataset gathered in a real-world clinical setting and compare to expert annotations, achieving similar levels of accuracy to the levels of inter- and intra-observer variation.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7d052186c1d16b28ab93c96ad873f99cTest

المؤلفون: Bridge, Christopher P., Rosenthal, Michael, Wright, Bradley, Kotecha, Gopal, Fintelmann, Florian, Troschel, Fabian, Miskin, Nityanand, Desai, Khanant, Wrobel, William, Babic, Ana, Khalaf, Natalia, Brais, Lauren, Welch, Marisa, Zellers, Caitlin, Tenenholtz, Neil, Michalski, Mark, Wolpin, Brian, Andriole, Katherine

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition

الوصف: The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::891a2f7a00e8c36611b0a580e6e29611Test

المؤلفون: Bridge, Christopher P.

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition

الوصف: The monogenic signal is an image analysis methodology that was introduced by Felsberg and Sommer in 2001 and has been employed for a variety of purposes in image processing and computer vision research. In particular, it has been found to be useful in the analysis of ultrasound imagery in several research scenarios mostly in work done within the BioMedIA lab at Oxford. However, the literature on the monogenic signal can be difficult to penetrate due to the lack of a single resource to explain the various principles from basics. The purpose of this document is therefore to introduce the principles, purpose, applications, and limitations of the methodology. It assumes some background knowledge from the fields of image and signal processing, in particular a good knowledge of Fourier transforms as applied to signals and images. We will not attempt to provide a thorough math- ematical description or derivation of the monogenic signal, but rather focus on developing an intuition for understanding and using the methodology and refer the reader elsewhere for a more mathematical treatment.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79888bb38d6f29f6c9fe0dd818205e0fTest