PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT

Ren, Jintao; Huynh, Bao Ngoc; Groendahl, Aurora Rosvoll; Tomić, Oliver; Futsæther, Cecilia Marie; Korreman, S.

doi:10.1007/978-3-030-98253-9_7

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…In, 59 Yousefirizi et al used a 3D nnU-Net with SE normalization trained on a leave-one-center-out with a combination of a "unified" focal and Mumford-Shah losses, leveraging the advantage of distribution, region, and boundary-based loss functions. Lastly, Ren et al 60 proposed a 3D nnU-Net with various PET normalization techniques, namely, PET-clip and PET-sin. The former clips the standardized uptake values (SUV) range in [0,5] and the latter transforms monotonic spatial SUV increase into onion rings via a sine transform of SUV, which ranked them fifth on the leaderboard.…”

Section: The Current State-of-the-art Methods For Handn Tumor Segment...mentioning

confidence: 99%

“…used a 3D nnU‐Net with SE normalization trained on a leave‐one‐center‐out with a combination of a “unified” focal and Mumford‐Shah losses, leveraging the advantage of distribution, region, and boundary‐based loss functions. Lastly, Ren et al 60 . proposed a 3D nnU‐Net with various PET normalization techniques, namely, PET‐clip and PET‐sin.…”

Section: Related Workmentioning

confidence: 99%

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SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images

Li,

Chen,

Jang

et al. 2023

Medical Physics

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BackgroundRadiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time‐consuming process. In recent years, deep convolutional neural networks (DCNN) have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long‐range dependency is still limited, and this can result in sub‐optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long‐range information in several semantic segmentation tasks performed on medical images.PurposeDespite the impressive representation capacity of vision transformer models, current vision transformer‐based segmentation models still suffer from inconsistent and incorrect dense predictions when fed with multi‐modal input data. We suspect that the power of their self‐attention mechanism may be limited in extracting the complementary information that exists in multi‐modal data. To this end, we propose a novel segmentation model, debuted, Cross‐modal Swin Transformer (SwinCross), with cross‐modal attention (CMA) module to incorporate cross‐modal feature extraction at multiple resolutions.MethodsWe propose a novel architecture for cross‐modal 3D semantic segmentation with two main components: (1) a cross‐modal 3D Swin Transformer for integrating information from multiple modalities (PET and CT), and (2) a cross‐modal shifted window attention block for learning complementary information from the modalities. To evaluate the efficacy of our approach, we conducted experiments and ablation studies on the HECKTOR 2021 challenge dataset. We compared our method against nnU‐Net (the backbone of the top‐5 methods in HECKTOR 2021) and other state‐of‐the‐art transformer‐based models, including UNETR and Swin UNETR. The experiments employed a five‐fold cross‐validation setup using PET and CT images.ResultsEmpirical evidence demonstrates that our proposed method consistently outperforms the comparative techniques. This success can be attributed to the CMA module's capacity to enhance inter‐modality feature representations between PET and CT during head‐and‐neck tumor segmentation. Notably, SwinCross consistently surpasses Swin UNETR across all five folds, showcasing its proficiency in learning multi‐modal feature representations at varying resolutions through the cross‐modal attention modules.ConclusionsWe introduced a cross‐modal Swin Transformer for automating the delineation of head and neck tumors in PET and CT images. Our model incorporates a cross‐modality attention module, enabling the exchange of features between modalities at multiple resolutions. The experimental results establish the superiority of our method in capturing improved inter‐modality correlations between PET and CT for head‐and‐neck tumor segmentation. Furthermore, the proposed methodology holds applicability to other semantic segmentation tasks involving different imaging modalities like SPECT/CT or PET/MRI. Code:https://github.com/yli192/SwinCross_CrossModalSwinTransformer_for_Medical_Image_Segmentation

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Section: The Current State-of-the-art Methods For Handn Tumor Segment...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images

Li,

Chen,

Jang

et al. 2023

Medical Physics

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Andrearczyk

Oreiller

Boughdad

et al. 2022

Lecture Notes in Computer Science

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Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images

Wang

Lombardo

Huang

et al. 2023

Lecture Notes in Computer Science

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PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT

Cited by 7 publications

References 16 publications

SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images

SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images

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