Class SegSummarizer

monai/auto3dseg/seg_summarizer.py:34–213

SegSummarizer serializes the operations for data analysis in Auto3Dseg pipeline. It loads two types of analyzer functions and execute differently. The first type of analyzer is CaseAnalyzer which is similar to traditional monai transforms. It can be composed with other transforms to

class SegSummarizer(Compose):
    """
    SegSummarizer serializes the operations for data analysis in Auto3Dseg pipeline. It loads
    two types of analyzer functions and execute differently. The first type of analyzer is
    CaseAnalyzer which is similar to traditional monai transforms. It can be composed with other
    transforms to process the data dict which has image/label keys. The second type of analyzer
    is SummaryAnalyzer which works only on a list of dictionary. Each dictionary is the output
    of the case analyzers on a single dataset.

    Args:
        image_key: a string that user specify for the image. The DataAnalyzer will look it up in the
            datalist to locate the image files of the dataset.
        label_key: a string that user specify for the label. The DataAnalyzer will look it up in the
            datalist to locate the label files of the dataset. If label_key is None, the DataAnalyzer
            will skip looking for labels and all label-related operations.
        do_ccp: apply the connected component algorithm to process the labels/images.
        hist_bins: list of positive integers (one for each channel) for setting the number of bins used to
            compute the histogram. Defaults to [100].
        hist_range: list of lists of two floats (one for each channel) setting the intensity range to
            compute the histogram. Defaults to [-500, 500].
        histogram_only: whether to only compute histograms. Defaults to False.

    Examples:
        .. code-block:: python

            # imports

            summarizer = SegSummarizer("image", "label")
            transform_list = [
                LoadImaged(keys=keys),
                EnsureChannelFirstd(keys=keys),  # this creates label to be (1,H,W,D)
                ToDeviced(keys=keys, device=device, non_blocking=True),
                Orientationd(keys=keys, axcodes="RAS"),
                EnsureTyped(keys=keys, data_type="tensor"),
                Lambdad(keys="label", func=lambda x: torch.argmax(x, dim=0, keepdim=True) if x.shape[0] > 1 else x),
                SqueezeDimd(keys=["label"], dim=0),
                summarizer,
            ]
            ...
            # skip some steps to set up data loader
            dataset = data.DataLoader(ds, batch_size=1, shuffle=False, num_workers=n_workers, collate_fn=no_collation)
            transform = Compose(transform_list)
            stats = []
            for batch_data in dataset:
                d = transform(batch_data[0])
                stats.append(d)
            report = summarizer.summarize(stats)
    """

    def __init__(
        self,
        image_key: str,
        label_key: str | None,
        average: bool = True,
        do_ccp: bool = True,
        hist_bins: list[int] | int | None = None,
        hist_range: list | None = None,
        histogram_only: bool = False,