HoVerNetPlus

class HoVerNetPlus(num_input_channels=3, num_types=None, num_layers=None)[source]

Initialise HoVer-Net+.

HoVer-Net+ takes an RGB input image, and provides the option to simultaneously segment and classify the nuclei present, aswell as semantically segment different regions or layers in the images. Note the HoVer-Net+ architecture assumes an image resolution of 0.5 mpp, in contrast to HoVer-Net at 0.25 mpp.

Initialise HoVer-Net+.

Parameters

  • num_input_channels (int) – The number of input channels, default = 3 for RGB.

  • num_types (int) – The number of types of nuclei present in the images.

  • num_layers (int) – The number of layers/different regions types present.

Methods

infer_batch

Run inference on an input batch.

postproc

Post processing script for image tiles.

Attributes

static infer_batch(model, batch_data, on_gpu)[source]

Run inference on an input batch.

This contains logic for forward operation as well as batch i/o aggregation.

Parameters

  • model (nn.Module) – PyTorch defined model.

  • batch_data (ndarray) – a batch of data generated by torch.utils.data.DataLoader.

  • on_gpu (bool) – Whether to run inference on a GPU.

static postproc(raw_maps)[source]

Post processing script for image tiles.

Parameters

raw_maps (list(ndarray)) – list of prediction output of each head and assumed to be in the order of [np, hv, tp, ls] (match with the output of infer_batch).

Returns

pixel-wise nuclear instance segmentation

prediction.

inst_dict (dict): a dictionary containing a mapping of each instance

within inst_map instance information. It has the following form inst_info = {

box: number[], centroids: number[], contour: number[][], type: number, prob: number,

} inst_dict = {[inst_uid: number] : inst_info} and inst_uid is an integer corresponds to the instance having the same pixel value within inst_map.

layer_map (ndarray): pixel-wise layer segmentation prediction. layer_dict (dict): a dictionary containing a mapping of each segmented

layer within layer_map. It has the following form layer_info = {

contour: number[][], type: number,

} layer_dict = {[layer_uid: number] : layer_info}

Return type

inst_map (ndarray)

Examples

>>> from tiatoolbox.models.architecture.hovernet_plus import HoVerNetPlus
>>> import torch
>>> import numpy as np
>>> batch = torch.from_numpy(image_patch)[None]
>>> # image_patch is a 256x256x3 numpy array
>>> weights_path = "A/weights.pth"
>>> pretrained = torch.load(weights_path)
>>> model = HoVerNetPlus(num_types=3, num_layers=5)
>>> model.load_state_dict(pretrained)
>>> output = model.infer_batch(model, batch, on_gpu=False)
>>> output = [v[0] for v in output]
>>> output = model.postproc(output)