Multi-disease classification of whole-body scintigraphy images based on deep learning

Liu, Liangxia; Cao, Ye; Lin, Qiang; Man, Zhengxing; Weiqiong, Wang

doi:10.1109/icsp54964.2022.9778791

Cited by 1 publication

(1 citation statement)

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“…For example, for organ segmentation, the U-Net CNN plateaued at 160 cases, while 1000 slice images are required to obtain at least 75% accuracy for lesion SPECT imaging [5,10]. In lesion segmentation, especially in SPECT images, majority of U-Net models only focused on dice similarity coefficient (DSC), accuracy, and precision [11,12].…”

Section: Introductionmentioning

confidence: 99%

Variation Segmentation Layer in Deep Learning Network for SPECT Images Lesion Segmentation

Mohd Akmal Masud,

Mohd Zamani Ngali,

Siti Amira Othman

et al. 2023

ARASET

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Functional imaging, particularly SPECT Iodine-131 ablation imaging, has gained recognition as a useful clinical tool for diagnosing, treating, assessing as well as avoiding a variety of disorders, which includes metastasis. Nonetheless, SPECT imaging is conspicuously characterized by low resolution, high sensitivity, limited specificity, and a low signal-to-noise ratio. This is caused by the imaging data's visually similar characteristics of lesions amongst diseases. Concentrating on the automated diagnosis of diseases with SPECT Iodine-131 ablation imaging, in this work, three types of segmentation layers are used. This comprises a pixel classification layer, dice classification layer, and focal loss layer that will be tested to determine which segmentation layer is high for auto-segmentation lesions on SPECT Iodine-131 ablation imaging. The data preprocessing, which mostly entails data augmentation, is initially carried out to address the issue of small SPECT image sample sizes by using the geometric transformation operation. Deep Designer Network App was used to develop a 3D U-Net Convolutional Neural Network (CNN). The dice classification layer shows the highest accuracy for the thyroid uptake data set, which is 42.34, 0.7333, and 0.5789 for RMSD, DSC, and IoU, respectively. There is significance in using the dice classification layer in data sets that have various forms of ground truth labeling. On the other hand, the pixel classification layer is promising and workable for the multi-disease, multi-lesion classification task of SPECT Iodine-131 ablation imaging with a huge data set training.

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