Replicate Demo and Results

MRM NeAt segmentation

We use 5-fold, 100 epochs, augmentation including resize, shift, scale and rotate for all methods. Details can be found in our paper.

The raw data file and other metrics (e.g., ASD) can be found in Excel table.

Fold (Dice)

Hipp

SC

Striatum

Tha

Weight file

Fold 1

0.9033 ± 0.0143

0.8765 ± 0.0215

0.9179 ± 0.0108

0.9276 ± 0.0087

Link (Code: l4oh)

Fold 2

0.9078 ± 0.0139

0.8646 ± 0.0190

0.9142 ± 0.0143

0.9259 ± 0.0055

Link (Code: l4oh)

Fold 3

0.9084 ± 0.0157

0.8741 ± 0.0271

0.9175 ± 0.0036

0.9327 ± 0.0104

Link (Code: l4oh)

Fold 4

0.9068 ± 0.0200

0.8710 ± 0.0404

0.9167 ± 0.0126

0.9237 ± 0.0089

Link (Code: l4oh)

Fold 5

0.9046 ± 0.0167

0.8712 ± 0.0260

0.9205 ± 0.0105

0.9295 ± 0.0049

Link (Code: l4oh)

Avg
(presented in the paper)

0.9062 ± 0.0146

0.8715 ± 0.0250

0.9174 ± 0.0100

0.9279 ± 0.0077

Ablation study loss curve

We provide tensorboard log files for ablation study experiments. link (Code: 5ygr).

(‘L_kl’ term represents L2 regularization in both MouseGAN and MouseGAN++, actually.)

How to display:

tensorboard --logdir=run1:MouseGAN++,run2:MouseGAN --host localhost  --port=6060
# open in the browser: http://localhost:6060/#scalars&_smoothingWeight=0.985
figure_loss_curve

Pretrained weight

Translation module:

Method

Dataset

Weight file

MouseGAN++

Multi-Modality Dataset

Link (Code: ummb)

MouseGAN

Multi-Modality Dataset

Link (Code: ummb)

StarGAN-v2

Multi-Modality Dataset

Link (Code: ummb)

Segmentation module:

Method

Dataset

Weight file

MouseGAN++

Multi-Modality Dataset (finetune on T1)

Link (Code: ummb)

MouseGAN++

Multi-Modality Dataset (finetune on T2)

Link (Code: ummb)

MouseGAN++

MRM NeAt Dataset

Link (Code: l4oh)

More examples

Some failed cases:

Rater study

We have invited three experts to score the synthetic images from 12 test mice with regard to the delineation of related anatomy on a 10-point scale as follows: excellent diagnostic quality (8-10), good diagnostic quality (6-8), fair diagnostic quality (4-6), poor diagnostic quality (2-4), and non-diagnostic (0-2). Please refer to the following table.

Real image (label)

MouseGAN++

MouseGAN

CycleGAN

SynSeg

UNIT

MUNIT

StarGAN-v2

Score
(mean ± std)

9.13 ± 0.72

7.56 ± 0.84

7.25 ± 0.84

4.58 ± 1.23

4.67 ± 1.15

4.36 ± 1.13

4.47 ± 1.50

4.67 ± 1.45

Dataset release

At the current stage, our dataset is provisionally available at https://pan.baidu.com/s/1CqukZN0ShIbRDtNWCHFr-w, and please send emails to Dr. Xiao-Yong Zhang at xiaoyong_zhang@fudan.edu.cn for the password. (Datasets are still being collated and packaged, and will be released formally via Zenodo.org with the publication of the paper.)

Contributing to MouseGAN++

We are happy about any contributions! (MRI data / trained weight / plug-in function code)

MouseGAN++ follows the open-access paradigm, allowing users to save their updated models and share their weights for use by the neuroimaging community.

Besides, the accumulation of additional imaging data will further improve the performance of MouseGAN++ and support the exploration of complex neuroimaging research.

Hyper-parameter

How to tune these hyper-parameters: The hyper-parameters matter and are task-dependent. They are not carefully selected yet. Despite this, the selection of hyper-parameters reported in our paper works well on mouse brain datasets.

In addition, we conducted an ablation study on contrastive loss hyper-parameters. We discovered that λ=1 works well on our task and maintains balance between other losses during the training procedure.

The quantitative results are provided as follows:

where λ=0 is the baseline method (previous MouseGAN).

Limitation

One limitation of our work is the dilemma caused by domain shifts, since the image quality and contrast from various centers may differ greatly. An appealing and promising solution is to convert our pretext task to cross-center image translation so that the learned center-agnostic features in the content space would alleviate the segmentation performance degradation.