Improving tumor co-segmentation on PET-CT images with 3D co-matting

Zhi, Zhao; Kim, Yusung; Zhou, Leixin; Plichta, Kristin A.; Allen, Bryan G.; Buatti, John M.; Wu, Xiaodong

doi:10.1109/isbi.2018.8363560

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…U-nets have had wide success in a variety of medical image segmentation problems, with subsequent modifications of U-nets also achieving success (Ronneberger et al 2015, Ö et al 2016, Milletari et al 2016, Xu et al 2018. However, their use has largely been restricted to CT and MRI modalities, with few studies implementing them on PET imaging (Blanc-Durand et al 2018, Zhong et al 2018. In the present study, many modifications were tested for the U-net structure, including extending to 3D and utilizing only 2D axial patches.…”

Section: Discussionmentioning

confidence: 99%

Comparison of 11 automated PET segmentation methods in lymphoma

et al. 2020

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Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple thresholding methods remain common despite the large heterogeneity in lymphoma lesion location, size, and contrast. Here, we assess 11 automated PET segmentation methods for their use in two scenarios: individual lesion segmentation and patient-level disease quantification in lymphoma. Lesions on 18F-FDG PET/CT scans of 90 lymphoma patients were contoured by a nuclear medicine physician. Thresholding, active contours, clustering, adaptive region-growing, and convolutional neural network (CNN) methods were implemented on all physician-identified lesions. Lesion-level segmentation was evaluated using multiple segmentation performance metrics (Dice, Hausdorff Distance). Patient-level quantification of total disease burden (SUVtotal) and metabolic tumor volume (MTV) was assessed using Spearman’s correlation coefficients between the segmentation output and physician contours. Lesion segmentation and patient quantification performance was compared to inter-physician agreement in a subset of 20 patients segmented by a second nuclear medicine physician. In total, 1223 lesions with median tumor-to-background ratio of 4.0 and volume of 1.8 cm3, were evaluated. When assessed for lesion segmentation, a 3D CNN, DeepMedic, achieved the highest performance across all evaluation metrics. DeepMedic, clustering methods, and an iterative threshold method had lesion-level segmentation performance comparable to the degree of inter-physician agreement. For patient-level SUVtotal and MTV quantification, all methods except 40% and 50% SUVmax and adaptive region-growing achieved a performance that was similar the agreement of the two physicians. Multiple methods, including a 3D CNN, clustering, and an iterative threshold method, achieved both good lesion-level segmentation and patient-level quantification performance in a population of 90 lymphoma patients. These methods are thus recommended over thresholding methods such as 40% and 50% SUVmax, which were consistently found to be significantly outside the limits defined by inter-physician agreement.

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Section: Discussionmentioning

confidence: 99%

Comparison of 11 automated PET segmentation methods in lymphoma

et al. 2020

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“…All of them are based on traditional methods and act as an auxiliary means of binary segmentation. For example, Zhong [10,11] adapts closed-form matting [14] to 3D, and uses alpha mattes as probability maps of tumors in calculating the region cost for PET-CT co-segmentation. Liu [12] also uses a 3D closed-form matting for organ model extraction for Virtual Human Project with significant efficiency improvement.…”

Section: Related Workmentioning

confidence: 99%

“…The previous works mainly focused on 2D medical images [2,8,9], and the existing research on 3D medical image matting is very limited in quantity and methodology. To the best of our knowledge, only [10,11,12,13] have touched upon the problem of the 3D matting, and they are mainly based on 2D closed-form matting [14]. There is no deep-learning-based method investigated.…”

Section: Introductionmentioning

confidence: 99%

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Wang¹,

Ye²,

Zhang³

et al. 2022

Preprint

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Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of many medical image labels. It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation. In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information, describe the structural characteristics of lesions more comprehensively, and thus benefit the subsequent diagnoses and analyses. In this work, we introduce image matting into the 3D scenes to describe the lesions in 3D medical images. The study of image matting in 3D modality is limited, and there is no high-quality annotated dataset related to 3D matting, therefore slowing down the development of data-driven deep-learning-based methods. To address this issue, we constructed the first 3D medical matting dataset and convincingly verified the validity of the dataset through quality control and downstream experiments in lung nodules classification. We then adapt the four selected state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images. Also, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark, which will be released to encourage further research 1 .

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“…All of them are based on traditional methods and are used as an auxiliary to binary segmentation. For example, Zhong et al adapted CF [20] to 3D, and used alpha mattes as probability maps of tumours in calculating the region cost for PET-CT co-segmentation [17,18]. Liu et al also used a 3D CF for organ model extraction for Virtual Human Project with significant efficiency improvement [19].…”

Section: Matting In 3d Scenariosmentioning

confidence: 99%

“…The 3D medical image matting research is very limited in quantity and methodology [11,12,13,14,15,16]. To the best of our knowledge, only [17,18,19] have touched upon the problem of the 3D matting, and these methods are all derived from 2D CF [20]. At present, there is no DL-based method that has been investigated.…”

Section: Introductionmentioning

confidence: 99%