Modelling brain lesion volume in patches with CNN-based Poisson Regression
Monitoring the progression of lesions is important for clinical response. Summary statistics such as lesion volume are objective and easy to interpret, which can help clinicians assess lesion growth or decay. CNNs are commonly used in medical image segmentation for their ability to produce useful features within large contexts and their associated efficient iterative patch-based training. Many CNN architectures require hundreds of thousands parameters to yield a good segmentation. In this work, an efficient, computationally inexpensive CNN is implemented to estimate the number of lesion voxels in a predefined patch size from magnetic resonance (MR) images. The output of the CNN is interpreted as the conditional Poisson parameter over the patch, allowing standard mini-batch gradient descent to be employed. The ISLES2015 (SISS) data is used to train and evaluate the model, which by estimating lesion volume from raw features, accurately identified the lesion image with the larger lesion volume for 86% of paired sample patches. An argument for the development and use of estimating lesion volumes to also aid in model selection for segmentation is made.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationMedical Image SegmentationModel SelectionregressionSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
The work presented explores the use of denoising autoencoders (DAE) for brain lesion detection, segmentation and false positive reduction. Stacked denoising autoencoders (SDAE) were pre-trained using a large number of un…
DenoisingLesion DetectionSegmentationTransfer LearningFeature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging
Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in training a voxel-level binary classifie…
Lesion DetectionOutlier DetectionSelf-supervised Brain Lesion Generation for Effective Data Augmentation of Medical Images
Accurate brain lesion delineation is important for planning neurosurgical treatment. Automatic brain lesion segmentation methods based on convolutional neural networks have demonstrated remarkable performance. However, n…
Data AugmentationLesion SegmentationSegmentationTriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images
The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy. Lesional volume estimation is usually performed by segmentation with de…
PrognosisSegmentationAcute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3…
Ischemic Stroke Lesion SegmentationLesion SegmentationSegmentationSensitivity+1