ivadomed / meeg-brainstorm Goto Github PK

Repository for training MEEG datasets with the ivadomed framework in Brainstorm

Python 100.00%

meeg-brainstorm's Introduction

ivadomed is an integrated framework for medical image analysis with deep learning.

The technical documentation is available here. The more detailed installation instruction is available there

Installation

ivadomed requires Python >= 3.7 and < 3.10 as well as PyTorch == 1.8. We recommend working under a virtual environment, which could be set as follows:

python -m venv ivadomed_env
source ivadomed_env/bin/activate

Install from release (recommended)

Install ivadomed and its requirements from Pypi <https://pypi.org/project/ivadomed/>__:

pip install --upgrade pip
pip install ivadomed

Install from source

Bleeding-edge developments builds are available on the project's master branch on Github. Installation procedure is the following:

git clone https://github.com/neuropoly/ivadomed.git
cd ivadomed
pip install -e .

Contributors

This project results from a collaboration between the NeuroPoly Lab and the Mila. The main funding source is IVADO.

List of contributors

Consult our Wiki(https://github.com/ivadomed/ivadomed/wiki) here for more help

meeg-brainstorm's People

Contributors

Stargazers

Watchers

Forkers

ambroiseodt tgnassou

meeg-brainstorm's Issues

Weight-based joint training/inference across EEG channels

Currently, we resample the channels (eg n=64 channels) into a Cartesian space (eg: 128x128 matrix) to use the dependency between channels (spatial proximity of electrodes might be useful to detect spikes) but it makes a lot of data and increases training time

We would like to find another way to recover this dependency without using the 2D representation.
We thought about putting weight on channels to have a joint training/inference but don’t know how to and which algorithm to use after.

An idea could be to associate neighbored channels by weight so that training is joint.
Distribution of weights could depend on times and depending on the spike zone, different channels would be gathered and features of their signals learned.

This is still an unfinished idea, any feedback or other approaches are welcome.
See below (colored circle represent weights)

Converter for Brainstorm Matlab trials to Numpy

Hey @AmbroiseOdonnat,

As discussed on the previous meeting, I created a converter from the Brainstorm matlab files (.mat) for you to be able to use them in python.

you can use the converter to start your training with the different approach on the signals level.

You can access the dataset on the signals level at:
~/duke/temp/konstantinos/EEG_signals

The equivalent dataset in NIFTI format that we use for training in Ivadomed exists in:
~/duke/temp/konstantinos/distant_spikes_2_seconds/data_Epilepsy_EEG_runs_3_small_space

Predictions concentrate at the center of the time domain

Just noticed the driving force of the low dice scores in the trained models.

All predictions are concentrated at the center of the time domain.

Here is an example of an overlap of all ground truths of the test set:

They are pretty much spread out in time (up-down axis on the first two views/columns)

However, if you take a look at the overlap of all predictions, they are all concentrated around the middle of the time dimension:

This appears to be consistent along all training configurations I tried.

@naga-karthik I just looked at your slide from yesterday and this behavior seems to hold there as well.

@andreanne-lemay @charleygros @mariehbourget any ideas on how to play with the config file to overcome this?

Here is an example config file used:

Config file


{
    "command": "train",
    "gpu_ids": [
        0
    ],
    "path_output": "/home/GRAMES.POLYMTL.CA/u111358/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_2_seconds/output_Epilepsy_EEG_runs_3_kernel_224_112",
    "model_name": "EEG_model",
    "debugging": true,
    "log_file": "log",
    "object_detection_params": {
        "object_detection_path": null,
        "safety_factor": [
            1,
            1,
            1
        ],
        "gpu_ids": 0,
        "path_output": "/home/GRAMES.POLYMTL.CA/u111358/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_2_seconds/output_Epilepsy_EEG_runs_3_kernel_224_112"
    },
    "loader_parameters": {
        "path_data": [
            "/home/GRAMES.POLYMTL.CA/u111358/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_2_seconds/data_Epilepsy_EEG_runs_3"
        ],
        "subject_selection": {
            "n": [],
            "metadata": [],
            "value": []
        },
        "target_suffix": [
            "_saw_EST_distant"
        ],
        "extensions": [],
        "roi_params": {
            "suffix": null,
            "slice_filter_roi": null
        },
        "contrast_params": {
            "training_validation": [
                "EEGspacedout2"
            ],
            "testing": [
                "EEGspacedout2"
            ],
            "balance": {}
        },
        "slice_filter_params": {
            "filter_empty_mask": false,
            "filter_empty_input": true
        },
        "slice_axis": "sagittal",
        "multichannel": false,
        "soft_gt": false,
        "is_input_dropout": false
    },
    "split_dataset": {
        "fname_split": null,
        "random_seed": 6,
        "split_method": "participant_id",
        "data_testing": {
            "data_type": null,
            "data_value": []
        },
        "balance": null,
        "train_fraction": 0.6,
        "test_fraction": 0.2
    },
    "training_parameters": {
        "batch_size": 128,
        "loss": {
            "name": "DiceLoss"
        },
        "training_time": {
            "num_epochs": 100,
            "early_stopping_patience": 50,
            "early_stopping_epsilon": 0.001
        },
        "scheduler": {
            "initial_lr": 0.001,
            "lr_scheduler": {
                "name": "CosineAnnealingLR",
                "base_lr": 1e-05,
                "max_lr": 0.01
            }
        },
        "balance_samples": {
            "applied": false,
            "type": "gt"
        },
        "mixup_alpha": null,
        "transfer_learning": {
            "retrain_model": null,
            "retrain_fraction": 1,
            "reset": true
        }
    },
    "default_model": {
        "name": "Unet",
        "dropout_rate": 0.3,
        "bn_momentum": 0.9,
        "depth": 3,
        "is_2d": true
    },
    "uncertainty": {
        "epistemic": false,
        "aleatoric": false,
        "n_it": 0
    },
    "postprocessing": {
        "remove_noise": {
            "thr": -1
        },
        "binarize_prediction": {
            "thr": 0.5
        },
        "uncertainty": {
            "thr": -1,
            "suffix": "_unc-vox.nii.gz"
        },
        "fill_holes": {},
        "remove_small": {
            "unit": "vox",
            "thr": 3
        }
    },
    "evaluation_parameters": {
        "target_size": {
            "unit": "vox",
            "thr": [
                20,
                100
            ]
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        "overlap": {
            "unit": "vox",
            "thr": 3
        }
    },
    "transformation": {
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            "size": [
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                32,
                224
            ]
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        "RandomAffine": {
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            "scale": [
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                0,
                0.1
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            "translate": [
                0,
                0,
                0.03
            ],
            "applied_to": [
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                "gt"
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            "dataset_type": [
                "training"
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        "NormalizeInstance": {
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            32,
            224
        ],
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            32,
            32,
            112
        ],
        "attention": false,
        "n_filters": 8
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Transform channels using z-score to mitigate noisy channels.

Data augmentation to account for missing channels

Each slice in time for the MEEG NIFTI files is created by an interpolation of the signals on the MEEG topography.
However, many times some channels need to be rejected because they are very noisy, and the interpolation from the neighbouring channels is filling in for them.

In order to account for this common practice and generalize training better, some channels can be randomly rejected. Since this is done on the channel level (not the NIFTI level), this needs to be enabled on the Brainstorm side right before converting MEEG signals to NIFTIs.

TODO:
Add a checkbox button for users to select if they want to enable this data augmentation.

THINGS TO CONSIDER:
should it be only a single channels to be dropped out, or multiple?

Transform EEG channels with z-score to mitigate noisy channels

Noisy channels affect significantly the topography of each NIFTI channels.

Explore potential solutions for making the fluctuations of noisy channels less influential to the overall topography.

One options would be to use the z-scores of the signal of each channel compared to a baseline before the trial.
Other option would be to remove those channels before converting to NIFTI in the first place

Discussion on training approaches

This thread will contain a list of each training that was performed on the same dataset, in order to optimize training.

The dataset is located at: ~/data_nvme_u111358/EEG-ivado/epilepsy/distant_spikes_5_seconds/data_Epilepsy_EEG_runs_3

Sampling rate is 100Hz.

Trials are comprised of 5 second signals (ie 500 samples) that contain 1 or 2 spikes within them. If there are 2 spikes within the trial, they need to be at least 3 seconds apart to make training easier.

The plan is to copy the config file that was used for the training, and a screenshot of the tensorboard training stats so we can comment on the changes that are needed on the config file

Consider a non-Cartesian space for the first two dimensions

Currently, we resample the channels (eg n=64 channels) into a Cartesian space (eg: 128x128 matrix). This has lots of disadvantages:

generates waaaay more data than there is originally
creates edge effect (partial volume), because the channels are represented as a disk, and every values outside the disk are set to zero.

An elegant solution would be to encode the coordinates of each channel and use this information to train a model. In this case, we would probably not use spatial convolutions anymore, but a variant of it.

Loss error during backward when using torch.nn.CrossEntropyLoss() with gpu

I am currently trying to use a gpu (cuda:0) on rosenberg server to run my code;
During training, the data are moved to the device cuda:0 but I have an error when doing the backward in the loss here https://github.com/AmbroiseOdonnat/MEEG-Brainstorm/blob/ao/seizure_classification/Train.py
I use torch.nn.CrossEntropyLoss() and I have the following error.
I precise that the code works perfectly on rosenberg and on my computer when I use a cpu.

)

Remaining issues identified by Konstantinos

1. channel coordinates are hardcoded and not absolutely perfect.

We need to go from channel coordinates to NIFTI slice pixel coordinates. This is needed in the case of partial annotations during inference. The coordinates are currently affected by the screen resolution of the computer. Right now the converter uses Brainstorm's functions for creating the 2d topography for each slice.
A more correct approach would be to take the coordinates of the maximum number of channels we can encounter, and assign them uniquely to a 32x32 image (Ivadomed likes images that are multiples of 16. If 32 is not enough, next size is 48x48). Sequentially interpolate on that 32x32 image and create with that process the NIFTI 3D volumes.
NOTE: the interpolation method needs to become a hyperparameter.

2. some model predictions/annotations are overlapping due to the overlapping sliding windows - maybe create a mask before assigning events?

3. Check if loading of the Ivadomed plugin should be done automatically after installation

Artificial shrinking of channel location during segmentation to avoid missing channels

This is a Brainstorm visualization issue/feature.

Brainstorm's EEG topography for a single time point looks like this:

One thing to notice is the placement of some channels on the perimetry of the skull.

The graphics of the perimeter are not getting updated while we scroll though the recording in time; only inside of it. The perimeter is always a solid line.

When I create the datasets, the annotations also don't include the perimeter.
This is not problematic during the creation of the BIDS NIFTI datasets.

However, this is problematic during evaluation.
In order to get from ivadomed NIFTI segmentations to Brainstorm events, I monitor the values of the pixels of each channel, and then they are converted to events in time.
Since the perimeter pixels are never included in the annotations, no segmentation is returned on those positions, therefore the perimeter channels are never annotated.

To alleviate this problem, I "shrink" the coordinate system by a few pixels relative to the center of the topography disc.
This is shown on the following figure:

The new positions are saved on a new file, without changing the original EEG/MEG datasets. The new positions are used during conversion from Ivadomed model annotations to Brainstorm events.

Although this is a "hardcoded" approach, it shouldn't affect the end result too much since the topography is interpolated from the channel positions in the first place.

Alternatives

A different approach will be to change the way that Brainstorm is handling these visualizations.

Consider Gaussian function for single pixel annotation

Currently, a window of 50 samples, centered around the epileptic peak, is labeled with value 1.

The problem with that, is that the model is exposed to values far away from the peak, with values 1.

A suggestion would be to, instead of using a step function, to use a Gaussian or Gamma function, centered around the peak.

We would use the SoftSeg training losses.

Create dataset with simulated data that have epileptic spikes originating from different locations

Currently we have a dataset from a single subject with an epileptic seizure source on the left medial-temporal lobe.

In order for the model to generalize to other epileptic centers, a simulated dataset needs to be created with shuffling of the epilepsy source location.

The approach for this would be:

Isolate an epileptic template spike (average all signals around the vicinity of the seizure source, around the timing of the spike)
Randomly place the seed in different sources in the Brainstorm environment. Check: https://neuroimage.usc.edu/brainstorm/Tutorials/Simulations
Annotate the new epileptic sources in space and time.
Create a new BIDS dataset for training
Train model

Segmentation on new trials/raw signals needs to undergo the same preprocessing steps as the trials that participated in training

There are two levels of signal datasets in Brainstorm:

Link to raw signals
Trials

The first one, as the name suggests, is just a link to the raw file that is created by the acquisition system.
There are no preprocessing steps on it. This link to the raw file has access to the entire duration of the recording.

Trials on the other hand, are specific segments in time of the recording. They are typically chosen around timings of interest (e.g. like a stimulus presentation).
The trials can be created just by chopping the raw recording, or they can be created after the raw files have undergone preprocessing steps (artifact removal, filtering, resampling etc.)
Typically, all trials should undergo preprocessing.

Ivadomed training:
The training of the Ivadomed model is being done on the trials level. So the preprocessing is already done.

When we have a trained model and we want to segment a new trial, it makes sense that the trials have to undergo the same preprocessing steps for the model to work.

For segmentation of the raw files, since a sliding window is creating new "trials", the same preprocessing needs to be applied.

Sugesstion
For all this to work seamlessly with ivadomed, all preprocessing steps will be logged to the config file and then applied to the newly created "trials" that the NIFTIs are based on.

I am already doing something similar by logging the trials filenames, sampling rate of the trials, and the Brainstorm parameters used on the process_ivadomed_create_dataset.m function.

Check:


"brainstorm": {
    "modality": "EEG",
    "event_for_ground_truth": "saw_EST_distant",
    "channel_drop_out": 2,
    "annotations_time_window": [
      -0.1,
      0.3
    ],
    "fs": 100,
    "jitter": 0.5,
    "soft_annotation_threshold": [],
    "bids_folder_creation_mode": "Separate each trial as different subjects",
    "sInputs": [
      "1: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial003_notch_band_resample.mat",
      "2: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial006_notch_band_resample.mat",
      "3: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial018_notch_band_resample.mat",
      "4: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial022_notch_band_resample.mat",
      "5: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial027_notch_band_resample.mat",
      "6: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial038_notch_band_resample.mat",
      "7: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial052_notch_band_resample.mat",
      "8: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial061_notch_band_resample.mat",
      "9: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial066_notch_band_resample.mat",
      "10: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial071_notch_band_resample.mat",
      "11: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial083_notch_band_resample.mat",
      "12: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial111_notch_band_resample.mat",
      "13: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial112_notch_band_resample.mat",
      "14: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial113_notch_band_resample.mat",
      "15: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial123_notch_band_resample.mat",
      "16: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial127_notch_band_resample.mat",
      "17: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial133_notch_band_resample.mat",
      "18: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial137_notch_band_resample.mat",
      "19: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial145_notch_band_resample.mat",
      "20: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial151_notch_band_resample.mat",
      "21: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial157_notch_band_resample.mat",
      "22: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial162_notch_band_resample.mat",
      "23: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial166_notch_band_resample.mat",
      "24: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial174_notch_band_resample.mat",
      "25: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial182_notch_band_resample.mat",
      "26: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial187_notch_band_resample.mat",
      "27: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial207_notch_band_resample.mat",
      "28: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial237_notch_band_resample.mat",
      "29: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial239_notch_band_resample.mat",
      "30: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial244_notch_band_resample.mat",
      "31: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial259_notch_band_resample.mat",
      "32: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial268_notch_band_resample.mat",
      "33: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial271_notch_band_resample.mat",
      "34: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial288_notch_band_resample.mat",
      "35: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial294_notch_band_resample.mat",
      "36: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial303_notch_band_resample.mat",
      "37: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial304_notch_band_resample.mat",
      "38: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial307_notch_band_resample.mat",
      "39: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial313_notch_band_resample.mat",
      "40: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial321_notch_band_resample.mat",
      "41: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial322_notch_band_resample.mat",
      "42: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial338_notch_band_resample.mat",
      "43: sub-pt0090/sub-pt0090_ses-20210722_task-rest_run-03_meg/data_spaced_out_5_trial341_notch_band_resample.mat"
    ]
  }

Update the README for external contributors to chip in

It is currently difficult to get a grasp of the project (what is being done, who is doing what, etc.)

This info should be in the README.

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