Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation

• URL: http://arxiv.org/abs/2510.08116v1
• Date: Thu, 09 Oct 2025 11:57:04 GMT
• Title: Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
• Authors: Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen,
• Abstract summary: We propose a CT-specific augmentation technique, called Random windowing, that exploits the available HU distribution of intensities in CT images.<n>We perform ablations and analysis of our method on multiple datasets, and compare to, and outperform, state-of-the-art alternatives.
• Score: 21.50697223751202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable automated medical image analysis for detecting and delineating tumors in CT images, thereby reducing clinicians' workload. Achieving generalization capabilities in limited data domains, such as radiology, requires modern DL models to be trained with image augmentation. However, naively applying augmentation methods developed for natural images to CT scans often disregards the nature of the CT modality, where the intensities measure Hounsfield Units (HU) and have important physical meaning. This paper challenges the use of such intensity augmentations for CT imaging and shows that they may lead to artifacts and poor generalization. To mitigate this, we propose a CT-specific augmentation technique, called Random windowing, that exploits the available HU distribution of intensities in CT images. Random windowing encourages robustness to contrast-enhancement and significantly increases model performance on challenging images with poor contrast or timing. We perform ablations and analysis of our method on multiple datasets, and compare to, and outperform, state-of-the-art alternatives, while focusing on the challenge of liver tumor segmentation.

Related papers

• CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection [28.766572737220482]
  We present CTForensics, a dataset designed to evaluate the generalization capability of CT forgery detection methods.<n>We also introduce ESF-CTFD, an efficient CNN-based neural network that captures forgery cues across the wavelet, spatial, and frequency domains.<n>Experiments demonstrate that ESF-CTFD consistently outperforms existing methods and exhibits superior generalization across different CT generative models.
  arXiv  Detail & Related papers  (2026-03-02T13:58:28Z)
• Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction [72.80209358480424]
  overdose of iodinated contrast media (ICM) can cause kidney damage and life-threatening allergic reactions.<n>Deep learning methods can generate CT images of normal-dose ICM from low-dose ICM, reducing the required dose.<n>We propose a Structure-constrained Language-informed Diffusion Model (SLDM) that integrates structural synergy and spatial intelligence.
  arXiv  Detail & Related papers  (2026-01-28T06:54:06Z)
• EqDiff-CT: Equivariant Conditional Diffusion model for CT Image Synthesis from CBCT [43.92108185590778]
  Cone-beam computed tomography (CBCT) is widely used for imageguided radiotherapy (IGRT)<n>We propose a novel diffusion-based conditional generative model, coined EqDiff-CT, to synthesize high-quality CT images from CBCT.
  arXiv  Detail & Related papers  (2025-09-26T05:51:59Z)
• Taming Stable Diffusion for Computed Tomography Blind Super-Resolution [20.195025131749944]
  High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure.<n>While deep learning methods have shown promise in CT super-resolution, they face challenges with complex degradations and limited medical training data.<n>We propose a novel framework that adapts Stable Diffusion for CT blind super-resolution.
  arXiv  Detail & Related papers  (2025-06-13T06:45:05Z)
• Synthetic CT image generation from CBCT: A Systematic Review [44.01505745127782]
  Generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology.<n>A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT.
  arXiv  Detail & Related papers  (2025-01-22T13:54:07Z)
• Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images [5.395912799904941]
  variations in CT scanner models and acquisition protocols introduce significant variability in the extracted radiomic features.<n> LTDiff++ is a multiscale latent diffusion model designed to enhance feature extraction in medical imaging.
  arXiv  Detail & Related papers  (2024-10-05T02:13:57Z)
• Anatomically constrained CT image translation for heterogeneous blood vessel segmentation [3.88838725116957]
  Anatomical structures in contrast-enhanced CT (ceCT) images can be challenging to segment due to variability in contrast medium diffusion. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. CycleGAN has attracted particular attention because it alleviates the need for paired data. We present an extension of CycleGAN to generate high fidelity images, with good structural consistency.
  arXiv  Detail & Related papers  (2022-10-04T16:14:49Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Segmentation of Lung Tumor from CT Images using Deep Supervision [0.8733639720576208]
  Lung cancer is a leading cause of death in most countries of the world. This paper approaches lung tumor segmentation by applying two-dimensional discrete wavelet transform (DWT) on the LOTUS dataset.
  arXiv  Detail & Related papers  (2021-11-17T17:50:18Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.