Pyramid context learning for object detection

Ding, Pengxin; Zhang, Jianping; Zhou, Huan; Zou, Xiang; Wang, Minghui

doi:10.1007/s11227-020-03168-3

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…This is important because it can be argued that anatomical information within the direct vicinity of a query voxel can be of great descriptive value, resolving local ambiguities (e.g. it is unlikely that tumor is detected in or near the lens) 22 . Integration of contextual information is therefore likely to enhance model performance.…”

Section: Discussionmentioning

confidence: 99%

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

Strijbis

Bloeme

Jansen

et al. 2021

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

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

Strijbis

Bloeme

Jansen

et al. 2021

Sci Rep

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“…The output of each branch is concatenated before passing through two dense layers (ReLu and softmax activation, respectively) to get an output of size two, representing non-tumor and tumor. To include larger-scale contextual information, a pyramid structure was implemented where two inputs (scale 0; 32 × 32 × 3 and scale 1; 64 × 64 × 3) are included for each of the three views [24]. The latter was first downsampled to 32 × 32 × 3 to fit in the network.…”

Section: Network Structurementioning

confidence: 99%

Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN

et al. 2022

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Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.

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“…It extracts useful local and global features from the input image by using a deep residual network (ResNet)[23,31] and feature pyramid network[22,24,98]. • Region proposal network.…”

mentioning

confidence: 99%

Human pose, hand and mesh estimation using deep learning: a survey

Toshpulatov

Lee

et al. 2022

J Supercomput

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Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.

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Pyramid context learning for object detection

Cited by 11 publications

References 38 publications

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

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

Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN

Human pose, hand and mesh estimation using deep learning: a survey

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