nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning

Alqaoud, Motaz; Plemmons, John; Feliberti, Eric; Dong, Siqin; Kaipa, Krishnanand N.; Fichtinger, Gabor; Xiao, Yiming; Audette, Michel

doi:10.1109/embc48229.2022.9871109

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…My results corroborate previous studies showing that self-configuring auto-segmentation models are highly valuable for clinical implementation. 1,2,20–29 Isensee et al developed the nnUNet model, which is a self-configuring model based on UNets. 2 While the nnUNet also self-configures its design options by considering the data and the computational resources, the processes by which the nnUNet chooses its design options are different from my approach.…”

Section: Introductionmentioning

confidence: 99%

Self-Configuring Capsule Networks for Brain Image Segmentation

Avesta

Hossain

Aboian

et al. 2023

Preprint

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When an auto-segmentation model needs to be applied to a new segmentation task, multiple decisions should be made about the pre-processing steps and training hyperparameters. These decisions are cumbersome and require a high level of expertise. To remedy this problem, I developed self-configuring CapsNets (scCapsNets) that can scan the training data as well as the computational resources that are available, and then self-configure most of their design options. In this study, we developed a self-configuring capsule network that can configure its design options with minimal user input. We showed that our self-configuring capsule netwrok can segment brain tumor components, namely edema and enhancing core of brain tumors, with high accuracy. Out model outperforms UNet-based models in the absence of data augmentation, is faster to train, and is computationally more efficient compared to UNet-based models.

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Section: Introductionmentioning

confidence: 99%

Self-Configuring Capsule Networks for Brain Image Segmentation

Avesta

Hossain

Aboian

et al. 2023

Preprint

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3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet

2024

Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities

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Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs

Kafali,

Shih,

et al. 2024

Magn Reson Mater Phy

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Objective Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. Materials and methods 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). Results ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. Discussion ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

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nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning

Cited by 5 publications

References 29 publications

Self-Configuring Capsule Networks for Brain Image Segmentation

Self-Configuring Capsule Networks for Brain Image Segmentation

3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs

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