Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma

Abstract

In retinoblastoma, accurate segmentation of ocular structure and tumor tissue is important when working towards personalized treatment. This retrospective study serves to evaluate the performance of multi-view convolutional neural networks (MV-CNNs) for automated eye and tumor segmentation on MRI in retinoblastoma patients. Forty retinoblastoma and 20 healthy-eyes from 30 patients were included in a train/test (N = 29 retinoblastoma-, 17 healthy-eyes) and independent validation (N = 11 retinoblastoma-, 3 healthy-eyes) set. Imaging was done using 3.0 T Fast Imaging Employing Steady-state Acquisition (FIESTA), T2-weighted and contrast-enhanced T1-weighted sequences. Sclera, vitreous humour, lens, retinal detachment and tumor were manually delineated on FIESTA images to serve as a reference standard. Volumetric and spatial performance were assessed by calculating intra-class correlation (ICC) and dice similarity coefficient (DSC). Additionally, the effects of multi-scale, sequences and data augmentation were explored. Optimal performance was obtained by using a three-level pyramid MV-CNN with FIESTA, T2 and T1c sequences and data augmentation. Eye and tumor volumetric ICC were 0.997 and 0.996, respectively. Median [Interquartile range] DSC for eye, sclera, vitreous, lens, retinal detachment and tumor were 0.965 [0.950–0.975], 0.847 [0.782–0.893], 0.975 [0.930–0.986], 0.909 [0.847–0.951], 0.828 [0.458–0.962] and 0.914 [0.852–0.958], respectively. MV-CNN can be used to obtain accurate ocular structure and tumor segmentations in retinoblastoma.

[Retracted] A Multi‐Thresholding‐Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

“…The dataset includes 23 patients (17 healthy and 27 RB eyes.). The performance of Multiview CNN can be evaluated using k -fold cross validation methods, and it concludes that the proposed method gives better results for segmenting the retinoblastomic image [ 8 ].…”

Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy

“…In contrast, for C3, the pre-constructed mask was determined by the foreground segmentation result of UNet. Our multi-view network was adapted from a previous classification study [ 16 ]. Batch normalization was applied after every (3 × 3) 2D convolution layer, before the non-linear activation function.…”

“…First, since UNet is a widely established CNN that is used for a variety of imaging-related problems [ 12 ] and since it was used in two other studies for combined lymph structure segmentation [ 9 , 13 ], we included a patch-based UNet variant as a baseline model configuration. Other works have suggested the use of voxel-classification methods for individual LN level segmentation using a 3D multi-scale network [ 14 ], as well as 2.5D (multi-view; MV) networks for several segmentation challenges (multiple sclerosis [ 15 ], ocular structures [ 16 ], abdominal lymph structures [ 17 ], head-and-neck tumors [ 18 ]). Because 2.5D networks may more effectively learn features in the presence of little data [ 19 ] and because voxel classification may better resolve local ambiguities near level transitions, a multi-view convolutional neural network (MV-CNN) was included as our second configuration.…”

Retinoblastoma

“…Strijbis et al developed an automated method for identifying tumors in eye MR images by a multi-view convolutional neural network [18]. The various parts of the eye are segmented to make it easier to identify the tumor.…”