LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation

• URL: http://arxiv.org/abs/2308.09026v1
• Date: Thu, 17 Aug 2023 14:56:08 GMT
• Title: LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
• Authors: Berke Doga Basaran, Weitong Zhang, Mengyun Qiao, Bernhard Kainz, Paul M. Matthews, Wenjia Bai
• Abstract summary: We present LesionMix, a novel and simple lesion-aware data augmentation method. It performs augmentation at the lesion level, increasing the diversity of lesion shape, location, intensity and load distribution. Experiments on different modalities and different lesion datasets, including four brain MR lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix achieves promising performance in lesion image segmentation.
• Score: 10.464109996943218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spatial and intensity transformations to improve the diversity of training images. They are often designed at the image level, augmenting the full image, and do not pay attention to specific abnormalities within the image. Here, we present LesionMix, a novel and simple lesion-aware data augmentation method. It performs augmentation at the lesion level, increasing the diversity of lesion shape, location, intensity and load distribution, and allowing both lesion populating and inpainting. Experiments on different modalities and different lesion datasets, including four brain MR lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix achieves promising performance in lesion image segmentation, outperforming several recent Mix-based data augmentation methods. The code will be released at https://github.com/dogabasaran/lesionmix.

Related papers

• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks [3.1133049660590615]
  We present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE) This approach yields an improved posterior distribution approximation compared to traditional Variational Autoencoders (VAE) Our method outperforms generative adversarial conditions, showcasing enhancements in image quality synthesis.
  arXiv  Detail & Related papers  (2023-11-17T11:56:53Z)
• EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model [4.057796755073023]
  We develop controllable diffusion models for medical image synthesis, called EMIT-Diff. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints.
  arXiv  Detail & Related papers  (2023-10-19T16:18:02Z)
• AMLP:Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation [67.97926983664676]
  Self-supervised masked image modeling has shown promising results on natural images. However, directly applying such methods to medical images remains challenging. We propose a novel self-supervised medical image segmentation framework, Adaptive Masking Lesion Patches (AMLP)
  arXiv  Detail & Related papers  (2023-09-08T13:18:10Z)
• Deepfake Image Generation for Improved Brain Tumor Segmentation [0.0]
  This work investigates the feasibility of employing deep-fake image generation for effective brain tumor segmentation. A Generative Adversarial Network was used for image-to-image translation and image segmentation using a U-Net-based convolutional neural network trained with deepfake images. Results show improved performance in terms of image segmentation quality metrics, and could potentially assist when training with limited data.
  arXiv  Detail & Related papers  (2023-07-26T16:11:51Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants [58.720142291102135]
  This study develops an algorithm for calculating bone growth in contact with metallic implants. Bone and implant tissue were manually segmented in the training data set. In terms of network accuracy, the model reached around 98%.
  arXiv  Detail & Related papers  (2022-04-22T08:17:20Z)
• CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation [38.785081940984135]
  We propose a simple data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Our method improves the segmentation accuracy compared with other simple data augmentation approaches.
  arXiv  Detail & Related papers  (2021-08-16T03:54:52Z)
• Positional Contrastive Learning for Volumetric Medical Image Segmentation [13.086140606803408]
  We propose a novel positional contrastive learning framework to generate contrastive data pairs. The proposed PCL method can substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting.
  arXiv  Detail & Related papers  (2021-06-16T22:15:28Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.