中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images
	Bencevic, Marin1,2; Qiu, Yuming2,3; Galic, Irena1; Pizurica, Aleksandra2
	2023
摘要	Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We present a general approach for training semantic segmentation neural networks on much smaller input sizes called Segment-then-Segment. To reduce the input size, we use image crops instead of downscaling. One neural network performs the initial segmentation on a downscaled image. This segmentation is then used to take the most salient crops of the full-resolution image with the surrounding context. Each crop is segmented using a second specially trained neural network. The segmentation masks of each crop are joined to form the final output image. We evaluate our approach on multiple medical image modalities (microscopy, colonoscopy, and CT) and show that this approach greatly improves segmentation performance with small network input sizes when compared to baseline models trained on downscaled images, especially in terms of pixel-wise recall.
关键词	biomedical images convolutional neural networks medical image segmentation semantic segmentation
DOI	10.3390/s23020633
发表期刊	SENSORS
卷号	23期号:2页码:16
通讯作者	Galic, Irena(irena.galic@ferit.hr)
收录类别	SCI
WOS记录号	WOS:000918965000001
语种	英语