Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 26, Issue 7, Pages 3261-3271Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2022.3164570
Keywords
Image segmentation; Positron emission tomography; Breast tumors; Feature extraction; Training; Standards; Bioinformatics; Breast tumor; convolutional neural network (CNN); normalization; positron emission tomography (PET); segmentation
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Funding
- SJTU Global Strategic Partnership Fund [2021 SJTU-USYD]
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This paper proposes an attentive transformation (AT)-based normalization method for PET tumor segmentation, which dynamically generates pixel-dependent learnable parameters to improve segmentation performance.
Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation. The normalization process is one of the key components for accelerating network training and improving the performance of the network. However, existing normalization methods either introduce batch noise into the instance PET image by calculating statistics on batch level or introduce background noise into every single pixel by sharing the same learnable parameters spatially. In this paper, we proposed an attentive transformation (AT)-based normalization method for PET tumor segmentation. We exploit the distinguishability of breast tumor in PET images and dynamically generate dedicated and pixel-dependent learnable parameters in normalization via the transformation on a combination of channel-wise and spatial-wise attentive responses. The attentive learnable parameters allow to re-calibrate features pixel-by-pixel to focus on the high-uptake area while attenuating the background noise of PET images. Our experimental results on two real clinical datasets show that the AT-based normalization method improves breast tumor segmentation performance when compared with the existing normalization methods.
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