An Automated Hybrid Transfer Learning System for Detection and Segmentation of Tumor in MRI Brain Images with UNet and VGG-19 Network

Sandhya, Sankaran; Kumar, M. Senthil; Chidhambararajan, B.

doi:10.1201/9781003194415-9

Cited by 1 publication

(16 citation statements)

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“…Fig 2 shows the standard 3D slicer (manual), automatically segmented cortex (green), medulla (red) and segmentation leakage (blue), a mixed portion of both cortex and medulla, registered over the reference T1 map. The proposed method showed better segmentation with less leakage (blue) for both cortex and medulla than the standard VGG16 [ 15 ], VGG19 [ 17 ], ResNet34 [ 18 ], and ResNet50 [ 19 ] as compared to standard 3D slicer manual segmentation.…”

Section: Resultsmentioning

confidence: 99%

“…The results show that the proposed RCM U-Net gives better segmentation results as compared to conventional CNNs i.e. VGG16 [ 15 ], VGG19 [ 17 ], ResNet34 [ 18 ], and ResNet50 [ 19 ] with U-Net as the backbone. Also, the conventional CNNs have more leakage segmentation than the proposed RCM U-Net as shown in zoomed image.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed network training was implemented on Python 3.8 by Keras 2.3.1 using TensorFlow 2.1.0 [ 26 ] as backend on Intel(R) Xeon(R) CPU, 128 GB RAM, and GPU NVIDIA GeForce GTX 1080Ti, with a batch size of 5 for 200 epochs with patience = 50 as early stopping criteria. The quality of the proposed RCM U-Net segmentation was compared with standard VGG16 [ 15 ], VGG19 [ 17 ], ResNet34 [ 18 ], and ResNet50 [ 19 ] with U-Net as backbone which was trained on the same hardware. The parameters and hyperparameters of each network were tuned after extensive experimentation.…”

Section: Methodsmentioning

confidence: 99%

“…The deep learning methods i.e. RCM U-Net (proposed method), VGG16 [ 15 ], VGG19 [ 17 ], ResNet34 [ 18 ], and ResNet50 [ 19 ], predicted the cortex and medulla masks which were used to extract the mean cortex and medulla T1 values. Additionally, we also performed ROI erosion for the deep learning-based cortex and medulla segmented masks to strongly match the hand-drawn gold standard ROIs.…”

Section: Methodsmentioning

confidence: 99%

“…The quality of the proposed RCM U-Net segmentation is compared with conventional DL methods i.e. VGG16 [ 15 ], VGG19 [ 17 ], ResNet34 [ 18 ], and ResNet50 [ 19 ] that all used U-Net as a backbone. The purpose of this work is to replace the gold standard manual ROI selection [ 1 , 4 , 6 ] over the cortex and medulla for estimation of cortico-medullary difference (ΔT1) with state of art automated segmentation approach.…”

Section: Introductionmentioning

confidence: 99%

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Validation of automatically measured T1 map cortico-medullary difference (ΔT1) for eGFR and fibrosis assessment in allograft kidneys

et al. 2023

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MRI T1-mapping is an important non-invasive tool for renal diagnosis. Previous work shows that ΔT1 (cortex-medullary difference in T1) has significant correlation with interstitial fibrosis in chronic kidney disease (CKD) allograft patients. However, measuring cortico-medullary values by manually drawing ROIs over cortex and medulla (a gold standard method) is challenging, time-consuming, subjective and requires human training. Moreover, such subjective ROI placement may also affect the work reproducibility. This work proposes a deep learning-based 2D U-Net (RCM U-Net) to auto-segment the renal cortex and medulla of CKD allograft kidney T1 maps. Furthermore, this study presents a correlation of automatically measured ΔT1 values with eGFR and percentage fibrosis in allograft kidneys. Also, the RCM U-Net correlation results are compared with the manual ROI correlation analysis. The RCM U-Net has been trained and validated on T1 maps from 40 patients (n = 2400 augmented images) and tested on 10 patients (n = 600 augmented images). The RCM U-Net segmentation results are compared with the standard VGG16, VGG19, ResNet34 and ResNet50 networks with U-Net as backbone. For clinical validation of the RCM U-Net segmentation, another set of 114 allograft kidneys patient’s cortex and medulla were automatically segmented to measure the ΔT1 values and correlated with eGFR and fibrosis. Overall, the RCM U-Net showed 50% less Mean Absolute Error (MAE), 16% better Dice Coefficient (DC) score and 12% improved results in terms of Sensitivity (SE) over conventional CNNs (i.e. VGG16, VGG19, ResNet34 and ResNet50) while the Specificity (SP) and Accuracy (ACC) did not show significant improvement (i.e. 0.5% improvement) for both cortex and medulla segmentation. For eGFR and fibrosis assessment, the proposed RCM U-Net correlation coefficient (r) and R-square (R2) was better correlated (r = -0.2, R2 = 0.041 with p = 0.039) to eGFR than manual ROI values (r = -0.19, R2 = 0.037 with p = 0.051). Similarly, the proposed RCM U-Net had noticeably better r and R2 values (r = 0.25, R2 = 0.065 with p = 0.007) for the correlation with the renal percentage fibrosis than the Manual ROI results (r = 0.3, R2 = 0.091 and p = 0.0013). Using a linear mixed model, T1 was significantly higher in the medulla than in the cortex (p<0.0001) and significantly lower in patients with cellular rejection when compared to both patients without rejection and those with humoral rejection (p<0.001). There was no significant difference in T1 between patients with and without humoral rejection (p = 0.43), nor between the types of T1 measurements (Gold standard manual versus automated RCM U-Net) (p = 0.7). The cortico-medullary area ratio measured by the RCM U-Net was significantly increased in case of cellular rejection by comparison to humoral rejection (1.6 +/- 0.39 versus 0.99 +/- 0.32, p = 0.019). In conclusion, the proposed RCM U-Net provides more robust auto-segmented cortex and medulla than the other standard CNNs allowing a good correlation of ΔT1 with eGFR and fibrosis as reported in literature as well as the differentiation of cellular and humoral transplant rejection. Therefore, the proposed approach is a promising alternative to the gold standard manual ROI method to measure T1 values without user interaction, which helps to reduce analysis time and improves reproducibility.

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Section: Resultsmentioning

confidence: 99%