Fetal Brain Tissue Annotation and Segmentation Challenge Results

Payette, Kelly; Li, Hongwei; Dumast, Priscille de; Licandro, Roxane; Jiang, Hui; Siddiquee, Md Mahfuzur Rahman; Xu, Daguang; Myronenko, Andriy; Líu, Hao; Pei, Yongmao; Wang, Lisheng; Peng, Ying; Xie, Jun; Zhang, Huiquan; Dong, Guohai; Hao, Fang; Wang, Guotai; Rieu, ZunHyan; Kim, Donghyeon; Kim, Hyun Gi; Karimi, Davood; Gholipour, Ali; Torres, Helena R.; Oliveira, Bruno; Vilaça, João L.; Yang, Lin; Avisdris, Netanell; Ben-Zvi, Ori; Bashat, Dafna Ben; Fidon, Lucas; Aertsen, Michaël; Vercauteren, Tom; Sobotka, Daniel; Alenyà, Mireia; Villanueva, Maria Inmaculada; Cámara, Óscar; Fadida, Bella Specktor; Joskowicz, Leo; Liao, Weibin; Lv, Yi; Li, Xuesong; Mazher, Moona; Qayyum, Abdul; Puig, Domènec; Kebiri, Hamza; Zhang, Zelin; Xu, Xiaowei; Wu, Dan; Liao, KuanLun; Wu, Yixuan; Chen, JinTai; Xu, Yinlong; Zhao, Li; Vasung, Lana; Menze, Bjoern; Cuadra, Meritxell Bach; Jakab, András

doi:10.48550/arxiv.2204.09573

Cited by 7 publications

(6 citation statements)

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“…We studied the automatic segmentation of fetal brain 3D MRIs into seven tissue types and brain extraction 45 . Fetal brain 3D MRIs from the FeTA dataset 23 , 25 were used for the evaluation. More details about the dataset used for the evaluation can be found in section Fetal brain 3D MRI used for the evaluation of automatic segmentation .…”

Section: Resultsmentioning

confidence: 99%

“…For the evaluation of automatic fetal brain segmentation we have used the publicly available FeTA dataset 23 , 25 (first and second release).…”

Section: Methodsmentioning

confidence: 99%

“…The 40 3D MRIs and original segmentations (as provided with the FeTA dataset) were inspected by two paediatric radiologists within our institutions, MA and PD, with more than 8 years of experience in segmenting fetal brains. Corrections of the segmentations were performed 26 – 28 to reduce the variability against the published segmentation guidelines that was released with the FeTA dataset 23 , 25 . Two volumes of spina bifida aperta cases ( and ) were excluded because the poor quality of the 3D reconstruction did not allow to segment them reliably for the seven tissue types.…”

Section: Methodsmentioning

confidence: 99%

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A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Fidon¹,

Viola²,

Mufti³

et al. 2022

Open Res Europe

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Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA.

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Section: Resultsmentioning

confidence: 99%

“…For the evaluation of automatic fetal brain segmentation we have used the publicly available FeTA dataset 23 , 25 (first and second release).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Fidon¹,

Viola²,

Mufti³

et al. 2022

Open Res Europe

Self Cite

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“…The optimal solution would require further training on large abnormal cohorts (e.g., similarly to Fidon et al (2021b) ) along with possible introduction of classification step and optimisation of the network architecture and parcellation protocol. Notably, the best reported performances in terms of average Dice from the FETA segmentation challenge Payette et al (2022) vary within 0.78-0.79 range (vs. 0.87-0.90 for BOUNTI performance540 Tab. 2), which highlights the advantages of using high quality consistent ground truth labels for training.…”

Section: Impact Of Anomaliesmentioning

confidence: 98%

BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI

Uus,

Kyriakopoulou,

Makropoulos

et al. 2023

Preprint

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Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain bounti tissue).

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“…These limitations are (i) low contrast caused by the narrow task-dependent distribution range of regions of interest (ROIs), (ii) the network representation preferences caused by the distribution variation inter images, low contrast, and unclear boundaries between soft tissues. The deep learning models has been widely used in segmentation of medical images [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Pseudo LABELIng Based 3DResUNet with DEep Supervision for the Kidney PArsing Segmentation Challenge

Qayyum

Khan

Mazher

2022

Advances in Transdisciplinary Engineering

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Three-dimensional (3D) integrated renal structures (IRS) segmentation is an important task based on computed tomography angiography (CTA) images for laparoscopic partial nephrectomy (LPN). Fine renal artery segmentation based on a CTA image is an important step for kidney disease diagnosis and pre-operative planning. However, it could be challenging due to large inter-anatomy variation, thin structures, small volume ratio, and small labeled dataset of the fine renal artery. The Kidney PArsing Challenge (KiPA) 2022 organizers launched a challenge for automatic segmentation of kidneys, renal tumors, arteries, and veins. In this paper, we have proposed a two-stage solution for the automatic segmentation of four kidney-related structures on CTA images. In the first stage, the 3DResUNet with deep supervision has been used to generate pseudo labels. The pseudo labels generated using validation images from the first stage along with training samples are used in nnUNet to obtain the final segmentation. The proposed solution produced an optimal performance for Multi-Structure Segmentation for Renal Cancer Treatment. The code will be publicly available at: https://github.com/RespectKnowledge/Pseudo-labeling_Segmentation_KiPA22_challenege.

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Fetal Brain Tissue Annotation and Segmentation Challenge Results

Cited by 7 publications

References 0 publications

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI

Pseudo LABELIng Based 3DResUNet with DEep Supervision for the Kidney PArsing Segmentation Challenge

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