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Reprinted from: Jishi Platform | Author: GlobalTrack
In semi-supervised medical image segmentation, there is an empirical mismatch problem between labeled and unlabeled data distributions. This paper proposes a simple approach to alleviate this problem-bidirectional copy-pasting of labeled and unlabeled data in a simple Mean Teacher architecture.
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
Paper link: https://arxiv.org/pdf/2305.00673.pdf
Source link: https://github.com/DeepMed-Lab-ECNU/BCP
Segmentation of internal structures from medical images such as CT or MRI is crucial for many clinical applications. Various techniques for medical image segmentation based on supervised learning have been proposed, which usually require a large amount of labeled data. However, due to the tedious and expensive process of manual contour drawing when annotating medical images, semi-supervised segmentation has received increasing attention in recent years and has become ubiquitous in the field of medical image analysis.
Generally, in the field of semi-supervised medical segmentation, labeled and unlabeled data are drawn from the same distribution. But in the real world, it is difficult to estimate accurate distributions from labeled data because they are scarce. Therefore, there is an empirical distribution mismatch between a large amount of unlabeled data and a very small amount of labeled data. Semi-supervised segmentation methods always try to train on labeled and unlabeled data symmetrically in a consistent manner. For example, sub-training is generated as labels, and unlabeled data is supervised in a pseudo-supervised manner. Mean Teacher-based algorithms employ a consistency loss to supervise unlabeled data with strong augmentation, similar to supervising labeled data with GT. ContrastMask applies dense contrastive learning on labeled and unlabeled data. But most existing semi-supervised algorithms use labeled and unlabeled data under different learning paradigms.
CutMix is a simple but powerful data processing method, also known as copy-paste (CP), which has the potential to encourage unlabeled data to learn common semantics from labeled data, because pixels in the same image share more semantics. near. In semi-supervised learning, enforcing consistency between weak-strong augmentation pairs of unlabeled data is widely used, and CP is often used as a strong augmentation. However, the existing CP methods do not consider the unlabeled data with poor CP, or simply copy the object in the labeled data as the foreground and paste it to another data. They neglect to design consistent learning strategies for both labeled and unlabeled data, which hinders their use in reducing distribution gaps. Meanwhile, CP tries to enhance the network generalization ability by increasing the diversity of unlabeled data, but it is difficult to achieve high performance since CutMix images are only supervised by low-precision pseudo-labels.
To alleviate the experience mismatch problem between labeled and unlabeled data, a successful design encourages unlabeled data to learn comprehensive common semantics from labeled data, while promoting distributional align. This paper achieves this by proposing a simple yet very effective bidirectional copy-paste (BCP) method. This method is instantiated in the Mean Teacher framework. Specifically, to train the student network, we augment the input by copy-pasting random crops from labeled images (foreground) to unlabeled images (background). Propagation augments the input by copy-pasting random crops from the five-annotated image (foreground) to the annotated image (background). The student network is supervised by the generated supervision information through bi-directional copy-paste between the unlabeled image pseudo-labels from the teacher network and the label map of the annotated images. These two blended images help the network learn common semantics between labeled and unlabeled data bidirectionally and symmetrically.
This article method
Define a 3D medical image as . The goal of semi-supervised semantic segmentation of medical images is to predict the locations of background and objects in per-voxel label-map indications. The training set contains labeled data and unlabeled data ( ), ie,,.
In the Mean Teacher architecture of this paper, two unlabeled images and two labeled images are randomly selected. Then go from copy-pasting a random block to generate a blended image, from to generate another blended image. Unlabeled images are able to learn comprehensive general semantics from both inward and outward directions from labeled images. The image and are then fed into the student network to predict the segmentation mask and . Segmentation masks are supervised by bidirectional copy-pasting of unlabeled image predictions and annotated image label maps from the teacher network.
Mean Teacher and training strategy
In the BCP framework of this paper, there is a teacher network and a student network. The student network is optimized by SGD, and the teacher network is an exponential moving average of the student network. Our training strategy consists of three steps: first pre-train a model using labeled data, and then use the pre-trained model as a teacher model to generate pseudo-labels for unlabeled images. In each cycle, the student network parameters are first optimized using SGD. Finally the teacher network parameters are updated using the exponential moving average of the student parameters.
Pre-training via copy-paste
In this paper, the labeled data is copy-pasted and augmented to train the supervised model. During the self-training process, the supervised model will generate pseudo-labels for the unlabeled data. This strategy has been proven to be effective in improving segmentation performance.
To perform copy-paste between a bunch of images, you first need to generate a zero-center mask, indicating that voxels are from foreground (0) or background (1) images. The size of the zero-valued area is . The two-way copy and paste process can be described as:
Two-way copy-paste supervision signal
To train the student network, supervision signals are also generated by BCP operations. The unlabeled image and the incoming teacher network compute the probability map:
The initial pseudo-labels are determined by a usual threshold of 0.5 for binarized segmentation tasks, or using argmax operation for multi-label segmentation tasks. The final pseudo-label consists of selecting the largest connected component, which effectively removes outlier pixels. Then a two-way copy-paste unlabeled image pseudo-label and annotated GT label is proposed to obtain the supervision signal.
Each input image to the student network consists of components from labeled and unlabeled images. Intuitively, GT masks for labeled images are usually more accurate than pseudo-labels for unlabeled images. Use to control the effect of unlabeled image pixels on the loss function:
Teacher network parameter update:
The Atrial Segmentation Challenge  dataset consists of 100 labeled 3D gadolinium-enhanced magnetic resonance image scans (GE MRI).
Here we choose UA-MT, SASSNet, DTC, URPC, MC-Net, SS-Net as comparison models. Experimental results at different label rates are given here. Table 1 shows the relevant experimental results. It can be seen that the method in this paper achieves the highest performance on all four evaluation indicators, which greatly exceeds the comparison model.
82 manually drawn abdominal CT enhanced volumes. Here V-Net, DAN, ADVNET, UA-MT, SASSNet, DTC and CoraNet are selected as comparison algorithms. Table 2 shows the relevant experimental results. Our method BCP achieves significant improvements on the Dice, Jaccard and 95HD metrics (i.e. over the second best by 3.24%, 4.28% and 1.16 respectively). These results were not subjected to any post-processing for fair comparison.
Four classes (i.e., background, right ventricle, left ventricle, and myocardium) segment the dataset, containing scans from 100 patients. Table 3 shows the relevant experimental results. BCP goes beyond SOTA methods. For a setting with a marking ratio of 5%, we obtain a huge performance improvement of up to 21.76% on the Dice metric
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