Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images

Liu, Ziping; Laforest, Richard; Mhlanga, Joyce; Fraum, Tyler J.; Itani, Malak; Dehdashti, Farrokh; Siegel, Barry A.; Jha, Abhinav K.

doi:10.48550/arxiv.2102.02975

Cited by 1 publication

(1 citation statement)

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“…Next, our sample size for training the method was limited. To address this issue, we performed data augmentation using flipping and rotation, but other approaches, such as using simulation-based studies ( 25 , 26 ) may be explored. Next, we note that the proposed method classifies each image pixel as belonging to only one region.…”

Section: Discussionmentioning

confidence: 99%

Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology

Zheng

Zayed

et al. 2023

Front. Cardiovasc. Med.

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IntroductionA reliable and automated method to segment and classify carotid artery atherosclerotic plaque components is needed to efficiently analyze multi-weighted magnetic resonance (MR) images to allow their integration into patient risk assessment for ischemic stroke. Certain plaque components such as lipid-rich necrotic core (LRNC) with hemorrhage suggest a greater likelihood of plaque rupture and stroke event. Assessment for presence and extent of LRNC could assist in directing treatment with impact upon patient outcomes.MethodsTo address the need to accurately determine the presence and extent of plaque components on carotid plaque MRI, we proposed a two-staged deep-learning-based approach that consists of a convolutional neural network (CNN), followed by a Bayesian neural network (BNN). The rationale for the two-stage network approach is to account for the class imbalance of vessel wall and background by providing an attention mask to the BNN. A unique feature of the network training was to use ground truth defined by both high-resolution ex vivo MRI data and histopathology. More specifically, standard resolution 1.5 T in vivo MR image sets with corresponding high resolution 3.0 T ex vivo MR image sets and histopathology image sets were used to define ground-truth segmentations. Of these, data from 7 patients was used for training and from the remaining two was used for testing the proposed method. Next, to evaluate the generalizability of the method, we tested the method with an additional standard resolution 3.0 T in vivo data set of 23 patients obtained from a different scanner.ResultsOur results show that the proposed method yielded accurate segmentation of carotid atherosclerotic plaque and outperforms not only manual segmentation by trained readers, who did not have access to the ex vivo or histopathology data, but also three state-of-the-art deep-learning-based segmentation methods. Further, the proposed approach outperformed a strategy where the ground truth was generated without access to the high resolution ex vivo MRI and histopathology. The accurate performance of this method was also observed in the additional 23-patient dataset from a different scanner.ConclusionIn conclusion, the proposed method provides a mechanism to perform accurate segmentation of the carotid atherosclerotic plaque in multi-weighted MRI. Further, our study shows the advantages of using high-resolution imaging and histology to define ground truth for training deep-learning-based segmentation methods.

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Section: Discussionmentioning

confidence: 99%