Head and neck multi‐organ auto‐segmentation on CT images aided by synthetic MRI

Liu, Yingzi; Lei, Yang; Fu, Yabo; Wang, Tonghe; Zhou, Jun; Jiang, Xiaojun; McDonald, Mark W.; Beitler, Jonathan J.; Curran, Walter J.; Liu, Tian; Yang, Xiaofeng

doi:10.1002/mp.14378

Cited by 41 publications

(50 citation statements)

References 37 publications

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“…Even if other modality images exist, they need to be co-registered at first in order to be contentconsistent. As an alternative, cross-modality image synthesis has been used to aid the segmentation process [129,131,257].…”

Section: Synthetic Image-aided Segmentationmentioning

confidence: 99%

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A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.

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Section: Synthetic Image-aided Segmentationmentioning

confidence: 99%

“…DL-based methods [128,[130][131][132][133][134] have achieved the-state-of-art performances in medical image segmentation, especially in multiorgan segmentation. In contrast to traditional methods that ultilize handcrafted features, DL-based methods adaptively explore representative features from medical images [135].…”

Section: Introductionmentioning

confidence: 99%

A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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“…49 Recent segmentation studies start using HD95 that measures 95% quantile of distance. [50][51][52][53][54] Its value quantifies the large segmentation error and are closer to the intuition on the contour discrepancy. Futures studies from other groups that include HD95 results might help for justification.…”

Section: Discussionmentioning

confidence: 52%

“…However, HD is known to be overly sensitive to noise and outliers on the contours 49 . Recent segmentation studies start using HD95 that measures 95% quantile of distance 50–54 . Its value quantifies the large segmentation error and are closer to the intuition on the contour discrepancy.…”

Section: Discussionmentioning

confidence: 99%

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

Lei

He²,

Yao

et al. 2020

Medical Physics

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Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer‐aided diagnosis (CAD). This work aims to develop a deep learning‐based method for breast tumor segmentation using three‐dimensional (3D) ABUS automatically. Methods For breast tumor segmentation in ABUS, we developed a Mask scoring region‐based convolutional neural network (R‐CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R‐CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross‐validation and 30 were used for hold‐out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland–Altman analysis. Results The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross‐validation and hold‐out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646 ± 1.191 and 1.665 ± 1.129 mm, 0.489 ± 0.406 and 0.475 ± 0.371 mm, 0.755 ± 0.755 and 0.751 ± 0.508 mm, and 0.672 ± 0.612 and 0.665 ± 0.729 mm. The mean volumetric difference (mean and ± 1.96 standard deviation) was 0.47 cc ([−0.77, 1.71)) for the cross‐validation and 0.23 cc ([−0.23 0.69]) for hold‐out test, respectively. Conclusion We developed a novel Mask scoring R‐CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning‐based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.

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“…Because of the many artifacts and the axial truncation on CBCT images of H&N sites, using DL methods directly to contour OARs and the target on CBCT images is very challenging. One study used Cycle-GAN to convert CBCT to synthetic MRI images, then combined the CBCT and synthetic MRI together to enhance the training of a DLbased multi-organ autosegmentation model [8]. Most studies of autosegmenting from CBCT for online ART, as well as the state-ofthe-art methods, still focus on DIR-based methods to get the deformation vector field (DVF) from warping the planning CT (pCT) to CBCT's anatomy, then applying the DVF to the contours on pCT to get the updated contours on CBCT [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning–Based CT-to-CBCT Deformable Image Registration for Autosegmentation in Head and Neck Adaptive Radiation Therapy

Liang¹,

Morgan²,

Nguyen³

et al. 2021

JoAIMS

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The purpose of this study is to develop a deep learning-based method that can automatically generate segmentations on conebeam computed tomography (CBCT) for head and neck online adaptive radiation therapy (ART), where expert-drawn contours in planning CT (pCT) images serve as prior knowledge. Because of the many artifacts and truncations that characterize CBCT, we propose to utilize a learning-based deformable image registration method and contour propagation to get updated contours on CBCT. Our method takes CBCT and pCT as inputs, and it outputs a deformation vector field and synthetic CT (sCT) simultaneously by jointly training a CycleGAN model and 5-cascaded Voxelmorph model. The CycleGAN generates the sCT from CBCT, while the 5-cascaded Voxelmorph warps the pCT to the sCT's anatomy. We compared the segmentation results to Elastix, Voxelmorph and 5-cascaded Voxelmorph models on 18 structures including target and organs-at-risk. Our proposed method achieved an average Dice similarity coefficient of 0.83 ± 0.09 and an average 95% Hausdorff distance of 2.01 ± 1.81 mm. Our method showed better accuracy than Voxelmorph and 5-cascaded Voxelmorph and comparable accuracy to Elastix, but with much higher efficiency. The proposed method can rapidly and simultaneously generate sCT with correct CT numbers and propagate contours from pCT to CBCT for online ART replanning.

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Head and neck multi‐organ auto‐segmentation on CT images aided by synthetic MRI

Cited by 41 publications

References 37 publications

A review of deep learning based methods for medical image multi-organ segmentation

A review of deep learning based methods for medical image multi-organ segmentation

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

Deep Learning–Based CT-to-CBCT Deformable Image Registration for Autosegmentation in Head and Neck Adaptive Radiation Therapy

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