Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes. For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters
In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. But when data is multiply annotated, sta…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationMixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images. However, most Network Architecture Search (NAS) approaches in medical imag…
DecoderImage SegmentationMedical Image AnalysisMedical Image Segmentation+2Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify the…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationBerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation
Medical image segmentation is a challenging task with inherent ambiguity and high uncertainty, attributed to factors such as unclear tumor boundaries and multiple plausible annotations. The accuracy and diversity of segm…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationCTS: A Consistency-Based Medical Image Segmentation Model
In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, co…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation