Can a single image processing algorithm work equally well across all phases of DCE-MRI?
Image segmentation and registration are said to be challenging when applied to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes rapid changes in intensity in the region of interest and elsewhere, which can lead to false positive predictions for segmentation tasks and confound the image registration similarity metric. While it is widely assumed that contrast changes increase the difficulty of these tasks, to our knowledge no work has quantified these effects. In this paper we examine the effect of training with different ratios of contrast enhanced (CE) data on two popular tasks: segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and VTN. We experimented further by strategically using the available datasets through pretraining and fine tuning with different splits of data. We found that to create a generalisable model, pretraining with CE data and fine tuning with non-CE data gave the best result. This interesting find could be expanded to other deep learning based image processing tasks with DCE-MRI and provide significant improvements to the models performance.
Code (0)
등록된 구현이 없습니다.
Tasks
AllImage RegistrationImage SegmentationSegmentationSemantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Wider Channel Attention Network for Remote Sensing Image Super-resolution
Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR). However, most CNN-based SR methods treat low-resolution (LR) inputs and featu…
Image Super-ResolutionSuper-ResolutionSaliency Driven Imagery Preprocessing for Efficient Compression -- Industrial Paper
The compression of satellite imagery remains an important research area as hundreds of terabytes of images are collected every day, which drives up storage and bandwidth costs. Although progress has been made in increasi…
Image CompressionA Neural Markovian Multiresolution Image Labeling Algorithm
This paper describes the results of formally evaluating the MCV (Markov concurrent vision) image labeling algorithm which is a (semi-) hierarchical algorithm commencing with a partition made up of single pixel regions an…
image-classificationImage ClassificationImage SegmentationSemantic SegmentationA Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
When it comes to clinical images, automatic segmentation has a wide variety of applications and a considerable diversity of input domains, such as different types of Magnetic Resonance Images (MRIs) and Computerized Tomo…
Heart SegmentationImage GenerationImage SegmentationMedical Image Segmentation+3Fuzzy clustering algorithms with distance metric learning and entropy regularization
The clustering methods have been used in a variety of fields such as image processing, data mining, pattern recognition, and statistical analysis. Generally, the clustering algorithms consider all variables equally relev…
ClusteringMetric Learning