---
title: CRL-2025 Fetal Brain Atlas
url: https://www.emergentmind.com/topics/crl-2025-fetal-brain-atlas
type: topic
---

# CRL-2025 Fetal Brain Atlas

Searching arXiv for the cited CRL-2025 atlas and closely related fetal atlas/segmentation papers to ground the article.
The **CRL-2025 Fetal Brain Atlas** is a spatiotemporal (4D) MRI atlas of typical in‑utero brain development from **21 to 37 gestational weeks (GW)**, assembled from carefully preprocessed fetal MRI acquired at Boston Children’s Hospital (2014–2023). It provides weekly age-specific templates, **36 tissue labels**, delineation of **transient white matter compartments** up to **31 GW**, and a **126-region parcellation**, together with a companion diffusion MRI atlas labeled in the same anatomical framework. The release also includes **multi-atlas segmentation (MAS)** tools, **deep learning–based multiclass segmentation models**, and pipelines for **single-subject** and **groupwise** analyses, positioning the atlas as a reference framework for spatial normalization, automatic segmentation and parcellation, volumetrics, morphometrics, cortical parcellations, tract segmentation, tractography, and multimodal developmental analysis [2508.15034].

## 1. Definition, scope, and advances over earlier CRL resources

CRL-2025 was introduced to establish a **high-quality, temporally consistent fetal brain atlas** for analysis across **mid to late gestation**. Its stated purpose is to support **spatial normalization**, **automatic segmentation/parcellation**, and **normative developmental analysis**. In contrast to the earlier CRL-2017 atlas, the new release emphasizes higher anatomical detail, broader labeling, improved image quality, and modern reconstruction and registration pipelines [2508.15034].

Several concrete advances are specified. First, anatomical detail and sharpness are improved, with **median edge sharpness improved by +0.14 to +0.39 across ages**. The paper gives representative comparisons: at **21 GW**, **CRL-2017 = 1.008** versus **CRL-2025 = 1.349**; at **35 GW**, **CRL-2017 = 0.605** versus **CRL-2025 = 0.799**. Second, label granularity is expanded to include **36 tissue labels**, **transient white matter compartments (WMC) up to 31 GW**, and **126 regional parcellation labels**. The release also refines CSF and cerebellar labeling through the addition of the **cavum septum**, **third/fourth ventricles**, and **bilateral vermis**, while removing or integrating small low-contrast labels such as the **hippocampal commissure** and **subthalamic nucleus** for robustness [2508.15034].

A further distinguishing feature is the paired **diffusion MRI atlas**, which is released with a newly created tissue segmentation and labels in the same anatomical framework. This makes the atlas explicitly multimodal at the resource level, even though the primary structural atlas is T2-weighted. In that sense, CRL-2025 consolidates template construction, label definition, segmentation tools, and downstream analytic infrastructure into a single atlas ecosystem rather than only distributing average images [2508.15034].

This suggests that CRL-2025 is best understood not merely as a template series, but as a coordinated reference space for **structural**, **diffusion**, and **segmentation-driven** fetal brain analysis.

## 2. Cohort, gestational-age coverage, and imaging protocols

The atlas was constructed from **160 fetuses** and **194 MRIs**, with some longitudinal sampling: **129 single-visit**, **28 two visits**, and **3 three visits ≥6 weeks apart**. Subjects were selected within **±1 week windows** around target atlas ages. Inclusion criteria required **pregnant participants aged 18–45**, no MRI contraindications, and exclusion of **multiple gestations** or any **suspected/known fetal brain abnormality** [2508.15034].

The structural atlas spans **21–37 GW**, and **weekly spatiotemporal templates are provided**. The underlying framework can generate atlases at **continuous ages**, but the distributed atlas grid is weekly. The companion diffusion atlas spans **22–38 GW**. The paper notes that fewer subjects at the extreme ages may reduce kernel support, and that transient white matter compartment labels are available only **up to 31 GW** because these compartments become indistinct later in gestation [2508.15034].

Structural MRI acquisition relied on **T2-weighted HASTE**. Reported scanner distribution was **Siemens 3T Trio (n=5), Skyra (n=134), Prisma (n=53), Vida (n=1)**, plus **one Philips 1.5T scan**. Coils were **18-channel body matrix until Aug 2017** and **30-channel thereafter**. T2-weighted acquisition parameters were reported as **TE = 115–120 ms**, **TR = 1400–1600 ms**, **flip = 120–160°**, **slice thickness = 2–3 mm**, and **in-plane resolution = 1 mm**, with matrix sizes **256×256–320×320**, field of view **256–320 mm²**, and **10–20 minutes acquisition per session** [2508.15034].

The diffusion protocol used **2–8 stacks per subject along orthogonal planes**, with **1–2 b=0** and **12 directions at b=500 s/mm²** per stack, **TR ≈ 3000–4000 ms**, **TE = 60 ms**, **in-plane resolution = 2 mm**, **slice thickness = 2–4 mm**, and **10–15 minutes per session**. Motion handling depended on **multi-stack fast 2D acquisitions**, followed by **retrospective slice-to-volume reconstruction (SVR)** and **motion-corrected dMRI reconstruction** [2508.15034].

A useful contextual comparison is that age-conditioned fetal atlas usage has also been exploited for segmentation in later work such as AtlasSeg, which pairs each subject with the nearest gestational-age template from the **“Spatiotemporal Atlas of the Fetal Brain”** and shows improved segmentation stability at early and late gestational ages [2411.02867]. That comparison is relevant because it clarifies that CRL-2025 belongs to a broader class of **GA-stratified reference spaces**, while offering denser labels and a paired diffusion resource.

## 3. Construction framework and temporal modeling

CRL-2025 uses a **temporally consistent 4D atlas** built with **stationary velocity free-form deformation (SVFFD) diffeomorphic registration (MIRTK)** integrated with **age-kernel regression** following Schuh et al. The method employs **iterative Log-Euclidean averaging of transformations** to enforce temporal coherence [2508.15034].

The age-continuous atlas is written as

$$
A(x,t) = \frac{\sum_i K_\sigma(t - t_i)\, I_i(\phi_i^{-1}(x))}{\sum_i K_\sigma(t - t_i)}.
$$

The Gaussian kernel is

$$
K_\sigma(\Delta t) = \exp\!\big(-\Delta t^2 / (2\sigma_t^2)\big),
$$

with normalized weights

$$
w_i(t)=g_i(t)/\sum_j g_j(t),
$$

and

$$
g_i(t)=\frac{1}{\sigma_t\sqrt{2\pi}\exp\!\big(-(t_i-t)^2/(2\sigma_t^2)\big).
$$

The paper further states that **$\sigma_t$ adapts to local age density**, and that weights are **truncated outside a kernel width of one week** to enforce locality. This produces an atlas that is continuous in age while remaining locally anchored to nearby gestational windows [2508.15034].

The iterative update is specified through a mean intensity estimate

$$
\bar{I}_k(t) = \sum_{i=1}^{n} w_i(t)\,\big(\tilde{I}_i \circ T_{k,i}^{-1}(t)\big),
$$

with **globally normalized** $\tilde{I}_i$ and

$$
T_{k,i}(t)=\bar{\varphi}_k(t)\circ\varphi_{k,i}(t).
$$

The subject-to-atlas warp at observed age $t_i$ is

$$
\phi_{k,i}=\exp(\mathbf{v}_{k,i}),
$$

where $\mathbf{v}_{k,i}$ minimizes the **SVFFD energy** registering $\tilde{I}_i$ to $\bar{I}_{k-1}(t_i)$. Longitudinal deformation from $t_i$ to $t$ is

$$
\psi_{k,i}(t)=\left(\prod_{s=1}^k \bar{\varphi}_s(t)\right)\left(\prod_{s=1}^k \bar{\varphi}_s(t_i)\right)^{-1},
$$

and age-adjusted subject transformations are

$$
\varphi_{k,i}(t)=\psi_{k-1,i}(t)\circ\phi_{k,i}.
$$

Residual atlas deformation is computed as the **Log-Euclidean mean of SVFs**,

$$
\bar{\varphi}_k(t)=\exp\!\Big(-\sum_i w_i(t)\,\log \varphi_{k,i}(t)\Big).
$$

The registration energy is given in illustrative **LDDMM/SVFFD** form as

$$
E = \int_0^1 \|L v_t\|^2\,dt + \lambda\, \mathcal{D}\!\big(I_0 \circ \phi_1^{-1}, I_1\big),
$$

where $v_t$ is the time-varying velocity field, $L$ is a differential smoothing operator, and $\mathcal{D}$ is a data term such as **normalized cross-correlation** or **SSD**. Optimization proceeds via a **multi-resolution schedule with gradient-based updates under MIRTK defaults**, and transformations are composed through the **Baker–Campbell–Hausdorff approximation** to remain within SVFFD [2508.15034].

This construction strategy places CRL-2025 within the line of spatiotemporal fetal atlases that seek not only sharp weekly templates but also **temporal coherence**. Related work on continuous implicit atlasing has addressed temporal inconsistency from a different angle, for example by representing the atlas as a continuous neural field over space and age [2209.06413]. CRL-2025, however, retains an explicitly diffeomorphic, kernel-regressed, population-atlas formulation grounded in classical atlas-building practice [2508.15034].

## 4. Label system, tissue compartments, and diffusion atlas

CRL-2025 provides three levels of anatomical representation. The first is **tissue segmentation**, reported as **36 labels**. These include the **cortical plate vs developing white matter boundary**, deep nuclei such as **lentiform** and **caudate**, **internal capsule**, **thalamus**, **hippocampus**, **brainstem**, **cerebellum** including **bilateral vermis**, major CSF spaces including **ventricles**, **3rd**, **4th**, and **cavum septum**, and the **corpus callosum** [2508.15034].

The second level is the delineation of **transient fetal white matter compartments** up to **31 GW**. These are explicitly listed as **cortical plate**, **subplate**, **intermediate zone**, and **ventricular and subventricular zones**, with the paper noting that these compartments become indistinct and disappear thereafter [2508.15034].

The third level is a **126-label regional parcellation**, spanning **bilateral cortical gyri/sulci/lobes** and **subcortical regions**, following the **Blesa et al. parcellation**. The paper further notes that the label hierarchy enables cortical parcellations by crossing tissue segmentation with regional labels [2508.15034].

Label generation combined propagation and expert editing. The process began with **ANTs symmetric diffeomorphic registration** from **CRL-2017 age-matched atlases**, followed by **MAS with Probabilistic STAPLE**, then **multi-round expert refinement in ITK-SNAP** under neurologist and neuroanatomist supervision. Each atlas required approximately **1 hour automatic processing + 2–4 hours manual editing** [2508.15034].

The companion **diffusion MRI atlas** was reconstructed using motion-tracking slice-to-volume registration, then aligned via **affine + non-linear registration** to age-specific templates. Tensors were reoriented with **finite strain**, and a **log-Euclidean tensor average** produced a spatiotemporal DTI atlas across **22–38 GW**. Derived maps include **color FA**, **FA**, **MD**, **RD**, **AD**, and **principal diffusion directions** [2508.15034]. The reported formulas are

$$
\bar{\lambda} = (\lambda_1 + \lambda_2 + \lambda_3)/3,
$$

$$
MD = (\lambda_1 + \lambda_2 + \lambda_3)/3,
$$

$$
AD = \lambda_1,\quad RD = (\lambda_2 + \lambda_3)/2.
$$

The FA formula is printed in the source with a truncated denominator, but FA is explicitly listed among the derived maps [2508.15034].

Label transfer to diffusion space used **diffeomorphic registration of the CRL age-equivalent T2w atlas to the DTI atlas via mean diffusivity**, followed by propagation of tissue classes and regional parcellations and then **manual refinement by experts**. The paper notes that tract delineations and tractography can be performed using these labels and DTI principal directions, and cites a related **white matter tract atlas (60 tracts, 23–36 GW)** as already available in related work [2508.15034].

This multimodal label strategy is significant because it places the structural and diffusion atlases into a **shared anatomical framework**. A plausible implication is that CRL-2025 can support analyses that move directly between volumetric tissue anatomy, regional parcellation, and tract-level diffusion organization without redefining the coordinate system or label ontology at each stage.

## 5. Segmentation tools and computational workflows

The release includes both **multi-atlas** and **deep learning** segmentation tools. The **single-subject T2w pipeline** is described as follows: acquire **multi-stack HASTE**, perform **SVR with SVRTK (defaults) or NiftyMIC (alpha=0.04)**, apply **N4 bias correction and intensity normalization**, perform **brain extraction (intracranial cavity segmentation)**, rigidly align to **age-matched atlas space with FLIRT**, then run either **MAS** or **DL inference**. In the MAS path, age-matched CRL-2025 atlases within **±1 week** are registered to the subject with **ANTs SyN (cross-correlation)**, labels are propagated, and fused with **Probabilistic STAPLE**. Optional manual refinement is performed in **ITK-SNAP**, and cortical parcellations can be derived by crossing tissue and regional labels [2508.15034].

For deep learning, the paper evaluates **nnU-Net**, **UNETR**, **Swin-UNETR**, and **EMM-Seg**. Input modality is reconstructed **T2w volumes** with training labels from **MAS with manual refinement**. Two datasets were used for model evaluation: **BCH**, with **177 subjects**, **20–38 GW**, and labels for **31 regions**; and **FeTA**, with **80 subjects**, **20–37 GW**, and **7 tissue classes** (**GM, DGM, WM, BS, CB, CSF, VEN**) [2508.15034].

The training setup is reported precisely: a **hybrid loss** of cross-entropy plus Dice,

$$
\mathcal{L}_\text{Dice} = 1 - \frac{2 \sum_i p_i g_i + \epsilon}{\sum_i p_i + \sum_i g_i + \epsilon},\quad \mathcal{L}=\mathcal{L}_\text{CE}+\epsilon\,\mathcal{L}_\text{Dice},
$$

with **AdamW**, **lr = 1e-4**, **PolyLR schedule**, **batch size = 1**, **200 epochs**, **PyTorch**, and **NVIDIA RTX A5000 GPU** [2508.15034].

Reported test-set performance is as follows:

| Model | BCH DSC / HD | FeTA DSC / HD |
|---|---:|---:|
| nnU-Net | 0.853±0.090 / 0.984±0.44 | 0.757±0.161 / 3.304±2.877 |
| UNETR | 0.821±0.101 / 1.33±0.64 | 0.764±0.165 / 3.941±2.934 |
| Swin-UNETR | 0.845±0.093 / 1.04±0.41 | 0.766±0.159 / 3.255±2.810 |
| EMM-Seg | 0.835±0.094 / 1.13±0.45 | 0.773±0.150 / 3.131±2.792 |

The paper states that performance is **strong on BCH** and **somewhat reduced on FeTA** because of **lower-quality reconstructions** and **occasional severe abnormalities**, and characterizes **EMM-Seg** as offering **good robustness and efficiency** [2508.15034].

CRL-2025 also sits within a wider shift toward atlas-informed and age-conditioned segmentation. AtlasSeg, for example, uses a **dual-U-Net** with a gestational-age-matched atlas image and atlas label as an explicit anatomical prior and reports **Average Dice: 0.9172 ± 0.0297** for cortical plate segmentation in-site [2411.02867]. Such work does not define CRL-2025 itself, but it clarifies how CRL-2025’s weekly templates and labels can function as inputs to atlas-guided learning systems rather than only as passive registration targets.

## 6. Quantitative evaluation, applications, and limitations

CRL-2025 is quantitatively evaluated at several levels. At the atlas-image level, **median edge sharpness** is higher than CRL-2017 at all ages, including examples such as **28 GW: 0.883 vs 1.160**, **34 GW: 0.632 vs 0.848**, and **37 GW: 0.622 vs 0.764**. At the segmentation level, boxplots across **31 regions** on BCH show high DSC for **cortical plate**, **subplate**, **CSF**, **ventricles**, **corpus callosum**, and **thalamus**, with lower performance on smaller or more challenging structures such as **amygdala**, **caudate**, and **fornix** [2508.15034].

The intended applications are broad. The atlas is explicitly positioned for **segmentation/parcellation of individual fetal MRIs**, **atlas-based label propagation**, **groupwise analysis**, **volumetrics**, **morphometrics**, **cortical parcellations**, **tract segmentation/tractography**, **multimodal analyses**, and **normative microstructural trajectories**. Example groupwise analysis is expressed through age-localized kernel regression of regional volumes,

$$
\hat{V}(t) = \frac{\sum_i K_\sigma(t - t_i)\, V_i}{\sum_i K_\sigma(t - t_i)},\quad K_\sigma(\Delta t)=\exp\!\big(-\Delta t^2/(2\sigma^2)\big),
$$

with uncertainty estimable by **bootstrap resampling** or **local weighted variance under the same kernel** [2508.15034].

The materials are publicly released through **Harvard Dataverse**, including the **T2w atlas DOI**, the **DTI atlas DOI**, and code repositories for both **MAS** and **deep learning segmentation**. Weekly atlases are distributed as **NIfTI volumes and label maps**, with **label key text files** for visualization in **ITK-SNAP** [2508.15034].

Several limitations are made explicit. Motion, artifacts, and variable stack quality remain limiting; reconstruction fidelity depends on the number and orientation of usable stacks. Age coverage is restricted to **21–37 GW** for T2w and **22–38 GW** for DTI, with fewer subjects at extreme ages. Deep models show reduced performance on **low-quality** or **markedly abnormal anatomy**, and **MAS remains recommended when geometry deviates substantially from typical development**. The atlas represents **typically developing brains**, so the paper advises caution in **clinical decision-making for atypical cases** [2508.15034].

These limitations are consistent with a broader literature in which age-conditioned atlases and conditional neural atlas models have been developed specifically to handle low-data or pathological settings, including ventriculomegaly and agenesis of the corpus callosum [2506.09668]. CRL-2025 itself is normative rather than pathology-specific, but the release structure suggests clear interoperability with such conditional atlas frameworks.

## 7. Position within fetal brain atlas research

CRL-2025 extends a lineage of spatiotemporal fetal atlas work represented in the paper’s cited resources, including **Gholipour et al. (2017)** and **Serag et al. (2012)**, while adding a denser label system, diffusion coupling, and released segmentation tools [2508.15034]. Its methodological center remains a **diffeomorphic, kernel-regressed, temporally coherent population atlas**, not a purely discriminative segmentation benchmark and not a purely neural implicit representation.

That distinction matters because contemporary fetal atlas research spans several paradigms. **AtlasSeg** operationalizes GA-matched atlas guidance inside a segmentation network [2411.02867]. **CAS-Net** jointly learns conditional atlas generation and segmentation with age-bin conditioning [2205.08239]. **CINA** and **CINeMA** pursue registration-light or registration-free implicit neural atlases that are continuous in age and can be conditioned on anatomy or pathology [2403.08550; 2506.09668]. CRL-2025 differs by delivering a curated, explicitly labeled, publicly released atlas-and-toolkit resource whose construction remains grounded in classical deformable atlas-building and expert label refinement rather than in end-to-end generative representation learning alone [2508.15034].

A common misconception is to treat CRL-2025 as only an updated image template. The released resource is broader: it includes **weekly structural atlases**, a **diffusion atlas**, **36 tissue labels**, **transient white matter compartments**, a **126-region parcellation**, **MAS**, **deep learning segmentation models**, and example workflows for **single-subject** and **groupwise** analysis [2508.15034]. Another possible misconception is to assume that the deep models supplant atlas-based methods in all settings. The paper is explicit that **MAS remains recommended** when anatomy is severely abnormal or motion/artifact is extreme [2508.15034].

In practice, CRL-2025 functions as a normative reference system for fetal MRI analysis between **21 and 37 GW**, with explicit support for segmentation, parcellation, morphometry, and diffusion-informed analysis. Its main contribution is not only improved anatomical sharpness relative to CRL-2017, but the consolidation of **template construction**, **expert-vetted labeling**, **multimodal alignment**, and **publicly released analysis tools** into a single spatiotemporal atlas framework [2508.15034].

Source: https://www.emergentmind.com/topics/crl-2025-fetal-brain-atlas