An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training

Abstract

Image segmentation is critical to lots of medical applications. While deep learning (DL) methods continue to improve performance for many medical image segmentation tasks, data annotation is a big bottleneck to DL-based segmentation because (1) DL models tend to need a large amount of labeled data to train, and (2) it is highly time-consuming and label-intensive to voxel-wise label 3D medical images. Significantly reducing annotation effort while attaining good performance of DL segmentation models remains a major challenge. In our preliminary experiments, we observe that, using partially labeled datasets, there is indeed a large performance gap with respect to using fully annotated training datasets. In this paper, we propose a new DL framework for reducing annotation effort and bridging the gap between full annotation and sparse annotation in 3D medical image segmentation. We achieve this by (i) selecting representative slices in 3D images that minimize data redundancy and save annotation effort, and (ii) self-training with pseudo-labels automatically generated from the base-models trained using the selected annotated slices. Extensive experiments using two public datasets (the HVSMR 2016 Challenge dataset and mouse piriform cortex dataset) show that our framework yields competitive segmentation results comparing with state-of-the-art DL methods using less than ∼20% of annotated data.

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

“…To segment 3D medical volumes with sparsely annotated 2D slices, Zheng et al [224] utilized uncertainty-guided self-training to gradually boost the segmentation accuracy. Before training segmentation models with sparsely annotated slices, Zheng et al [225] first identified the most influential and diverse slices for manual annotation with a deep network. After manual annotation of the selected slices, they conducted segmentation with a self-training strategy.…”

Segmentation Technology of Nucleus Image Based on U-Net Network

“…In the experiment, ASPP was changed to 4 parallel 3 × 3 expansion convolution operations, rates � (1,6,12,18). At the same time, during network training, random gradient descent (SGD) is used for parameter optimization.…”

“…Compared with image segmentation of natural scenes with clear outlines, medical images have great particularities. e complexity of the medical image itself causes the separation between its components to be blurred, and the boundaries between the components are not clear enough [6].…”

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

“…With the rapid development of deep learning networks in recent years, it has begun to be applied in image segmentation and gradually promoted and improved [14] [15] .Reference [16] proposed an image segmentation method based on the Convolutional Neural Network (CNN) loss function, which directly reduces the Hausdorff distance and improves the segmentation accuracy.Reference [17] proposed an image super-resolution method using progressive generation of confrontation networks. Convert a lower-resolution image into a higher-resolution image that can be dynamically scaled.…”