Recurrent residual U-Net with EfficientNet encoder for medical image segmentation

Siddique, Nahian; Sidike, Paheding; Alom, Zahangir; Devabhaktuni, V.K.

doi:10.1117/12.2591343

Cited by 11 publications

(7 citation statements)

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“…To generate a three-dimensional segmentation, inference was performed on all slices in a volume and then combined. The model architecture was based on the encoder-decoder U-Net (14), with EfficientNet comprising the encoder component (15); Ef-ficientNet has shown top performance on various image segmentation tasks (16). Further details on the model architecture are shown in Table E2 and Figure E1 (supplement).…”

Section: Data Stratificationmentioning

confidence: 99%

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Goel

Shih

Riyahi

et al. 2022

Radiology: Artificial Intelligence

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This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P , .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time.ClinicalTrials.gov identification no.: NCT00792155

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Section: Data Stratificationmentioning

confidence: 99%

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Goel

Shih

Riyahi

et al. 2022

Radiology: Artificial Intelligence

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“…Apart from this quantitative analysis, our study also performed a qualitative analysis of the autosegmentation results, which supported the clinical utility of the automated CNN segmentation results. N u m e r o u s s t u d i e s h a v e p r o p o s e d s p e c i a l i z e d architectures and training scheme modifications to achieve competitive segmentation (21)(22)(23), but it remains difficult to improve upon the basic U-Net if the corresponding training procedure is designed adequately (16). Thus, U-Net was chosen as the foundation of our model.…”

Section: Discussionmentioning

confidence: 99%

Development and acceptability validation of a deep learning-based tool for whole-prostate segmentation on multiparametric MRI: a multicenter study

Xu¹,

Zhang²,

Zhang³

et al. 2023

Quant Imaging Med Surg

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Background: Accurate whole prostate segmentation on magnetic resonance imaging (MRI) is important in the management of prostatic diseases. In this multicenter study, we aimed to develop and evaluate a clinically applicable deep learning-based tool for automatic whole prostate segmentation on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI).Methods: In this retrospective study, 3-dimensional (3D) U-Net-based models in the segmentation tool were trained with 223 patients who underwent prostate MRI and subsequent biopsy from 1 hospital and validated in 1 internal testing cohort (n=95) and 3 external testing cohorts: PROSTATEx Challenge for T2WI and DWI (n=141), Tongji Hospital (n=30), and Beijing Hospital for T2WI (n=29). Patients from the latter 2 centers were diagnosed with advanced prostate cancer. The DWI model was further finetuned to compensate for the scanner variety in external testing. A quantitative evaluation, including Dice similarity coefficients (DSCs), 95% Hausdorff distance (95HD), and average boundary distance (ABD), and a qualitative analysis were used to evaluate the clinical usefulness.Results: The segmentation tool showed good performance in the testing cohorts on T2WI (DSC: 0.922 for internal testing and 0.897-0.947 for external testing) and DWI (DSC: 0.914 for internal testing and 0.815 for external testing with fine-tuning). The fine-tuning process significantly improved the DWI model's performance in the external testing dataset (DSC: 0.275 vs.

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“…The experimental analyses noted that ResNet-101 achieved better sensitivity (recall) of 85.18% and accuracy of 97.12%. Siddique et al [6], furnished an image segmentation model attributed to the deep learning U-Net framework along with a pre-trained EfficientNet model. To enhance gradient learning and build a deeper U-Net model, residual connection and recurrent feedback with EfficientNet as an encoder was proposed.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Salian

Sawarkar

2023

Adv. technol. innov.

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The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F1-score of 88%.

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Recurrent residual U-Net with EfficientNet encoder for medical image segmentation

Cited by 11 publications

References 0 publications

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Development and acceptability validation of a deep learning-based tool for whole-prostate segmentation on multiparametric MRI: a multicenter study

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

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