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NuHTC: A Hybrid Task Cascade for Nuclei Instance Segmentation and Classification

NuHTC 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# NuHTC: A Hybrid Task Cascade for Nuclei Instance Segmentation and Classification

<p align="center">
  <a href="https://www.sciencedirect.com/science/article/pii/S1361841525001422">Paper</a> |
  <a href="#-docker">Docker</a> |
  <a href="#-setup-environment">Setup</a> |
  <a href="#-quick-start">Quick start</a> |
  <a href="#-preprocessing-data">Preprocessing</a> |
  <a href="#-train">Train</a> |
  <a href="#-test">Test</a> |
  <a href="#-infer">Infer</a> |
  <a href="#-segment-the-whole-slide-image">WSI Segmentation</a> |
  <a href="#-extract-the-nuclei-feature">Feature Extraction</a> |
  <a href="#-citation">Citation</a>
</p>

![](https://raw.githubusercontent.com/boyden/NuHTC/main/resources/nuhtc.jpg)
This repo is the official implementation of NuHTC.

**Browser demo on the Hugging Face Space:** [Web demo](https://huggingface.co/spaces/boyden/NuHTC)

## Overlaid Segmentation and Classification Prediction

The demo may take around 10s to load.
![](https://raw.githubusercontent.com/boyden/NuHTC/main/resources/instance_demo.gif)

## 🐳 Docker

The official image is on GitHub Container Registry. No local Python environment or separate weight download is required. See [`DOCKER.md`](./DOCKER.md) for details.

### Pull & run

```shell
docker pull ghcr.io/boyden/nuhtc:latest
```

### Patch-level inference

```shell
docker run --gpus all --rm --shm-size=32g \
  -v /path/to/your/pngs:/data/imgs \
  -v /path/to/output:/data/imgs_infer \
  ghcr.io/boyden/nuhtc:latest -c "python tools/infer.py /data/imgs \
    models/htc_lite_PanNuke_infer.py \
    models/pannuke.pth --output /data/imgs_infer"
```

### WSI inference

```shell
# Patches at 256×256 (~40×, stride 192).
docker run --gpus all --rm --shm-size=32g \
  -v /path/to/your/input:/data/wsi \
  -v /path/to/output:/data/wsi_infer \
  ghcr.io/boyden/nuhtc:latest -c "python tools/infer_wsi.py /data/wsi \
    models/htc_lite_PanNuke_infer.py \
    models/pannuke.pth --patch --seg --stitch \
    --patch_size 256 --step_size 192 --batch_size 16 \
    --save_dir /data/wsi_infer --mode qupath"
```

After segmentation, merge cross-patch overlapping nuclei (mask-NMS) so each cell appears once in the GeoJSON:

```shell
docker run --gpus all --rm \
  -v /path/to/output:/data/wsi_infer \
  ghcr.io/boyden/nuhtc:latest -c "python tools/nuclei_merge.py \
    --geojson /data/wsi_infer/nuclei/<SLIDE_ID>/<SLIDE_ID>.geojson \
    --overlap_threshold 0.05 --merge_strategy probability"
```

Load the `<SLIDE_ID>_merged.geojson` version in QuPath for cleaner overlays.

## 👉 Setup Environment

Set up the Python environment

```shell script
# Note, please follow the env.
conda create -n nuhtc -y python=3.10 
conda activate nuhtc
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
# or
# pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt
pip install mmcv-full==1.7.2 -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.13/index.html
python -m pip install histomicstk==1.2.10 --find-links https://girder.github.io/large_image_wheels -i https://pypi.org/simple
```

## 👉 Quick start

When setup is finished, download `pannuke.pth` from [Google Drive](https://drive.google.com/drive/folders/1MezZrVwx7S6MNYkpMO5ja2D6KcZkRvYo?usp=sharing) and save it as `models/pannuke.pth`. Run:

```shell script
python tools/infer.py demo/imgs configs/nuhtc/htc_lite_swin_pytorch_fpn_PanNuke_seasaw_CAS.py models/pannuke.pth --out demo/imgs_infer
```

Segmentation overlays are saved as PNGs in `demo/imgs_infer`. Open that folder to view results.

## 👉 Preprocessing data

First please download and unzip the files from [PanNuke dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke), where the folder structure should look like this:

```
NuHTC
├── ...
├── datasets
│   ├── PanNuke
│   │   ├── images
│   │   │   ├── fold1
│   │   │   │   ├── images.npy
│   │   │   ├── fold2
│   │   │   │   ├── images.npy
│   │   │   ├── fold3
│   │   │   │   ├── images.npy
│   │   ├── masks
│   │   │   ├── fold1
│   │   │   │   ├── masks.npy
│   │   │   │   ├── types.npy
│   │   │   ├── fold2
│   │   │   │   ├── masks.npy
│   │   │   │   ├── types.npy
│   │   │   ├── fold3
│   │   │   │   ├── masks.npy
│   │   │   │   ├── types.npy
├── ...
```

For the coco format annotation, please download the `coco` folder json file from [Google Drive](https://drive.google.com/drive/folders/1MezZrVwx7S6MNYkpMO5ja2D6KcZkRvYo?usp=sharing)

```
NuHTC
├── ...
├── coco
│   ├── PanNuke
│   │   ├── PanNuke_annt_RLE_fold1.json
│   │   ├── PanNuke_annt_RLE_fold2.json
│   │   ├── PanNuke_annt_RLE_fold3.json
├── ...
```

Then, generate `png` files for training and testing.

```python
import os
import numpy as np
from PIL import Image
from tqdm import tqdm

basedir = './datasets/PanNuke'
for fold in range(3):
    print(f'Preprocessing images: fold{fold+1}')
    imgdir = f'{basedir}/images/fold{fold+1}'
    img_data = np.load(f'{imgdir}/images.npy', mmap_mode='r')
    for i in tqdm(range(img_data.shape[0])):
        img = Image.fromarray(img_data[i].astype(np.uint8))
        os.makedirs(f'{basedir}/rgb', exist_ok=True)
        if not os.path.exists(f'{basedir}/rgb/fold{fold+1}_{i+1}.png'):
            img.convert('RGB').save(f'{basedir}/rgb/fold{fold+1}_{i+1}.png')

for fold in range(3):
    print(f'Preprocessing masks: fold{fold+1}')
    imgdir = f'{basedir}/masks/fold{fold+1}'
    img_data = np.load(f'{imgdir}/masks.npy', mmap_mode='r')
    for i in tqdm(range(img_data.shape[0])):
        img = 1 - img_data[i, :, :, 5]
        img = Image.fromarray(img.astype(np.uint8))
        os.makedirs(f'{basedir}/rgb_seg', exist_ok=True)
        if not os.path.exists(f'{basedir}/rgb_seg/fold{fold+1}_{i+1}.png'):
            img.save(f'{basedir}/rgb_seg/fold{fold+1}_{i+1}.png')
```

## 👉 Train

This is an example of training NuHTC on the first fold. To train on other folds, update the `fold = 1` content in `htc_lite_swin_pytorch_fpn_PanNuke_seasaw_CAS.py` to other folds.

```shell script
CUDA_VISIBLE_DEVICES=0 python tools/train.py configs/nuhtc/htc_lite_swin_pytorch_fpn_PanNuke_seasaw_CAS.py --no-validate
```

> Note, recent update (~May 2024, driver version 555.85, 555.99, 556.12) of Nvidia driver may lead to `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf8 in position 0: invalid start byte` in init wandb package. If your Nvidia driver version is greater than `552.44`, downgrade to the `Nvidia 552.44 studio driver` or update to a version newer than `560.70` to train the models successfully. For more details, see the [wandb issue](https://github.com/wandb/wandb/issues/7683).

## 👉 Test

``` shell script
CONFIG_NAME=htc_lite_swin_pytorch_fpn_PanNuke_seasaw_CAS.py
WEIGHT_BASE_PATH=work_dirs/htc_lite_swin_pytorch_seasaw_FPN_AttenROI_thres_96_base_aug_cas_PanNuke_full_epoch_200_fold1

# predict nuclei from images
CUDA_VISIBLE_DEVICES=0 python tools/test.py $WEIGHT_BASE_PATH/$CONFIG_NAME $WEIGHT_BASE_PATH/latest.pth \
--eval bbox --samples_per_gpu 16 \
--eval-options save=True format=pannuke save_path=$WEIGHT_BASE_PATH overlay=False

# calculate the metric
python tools/analysis_tools/pannuke/compute_stats.py --true_path=datasets/PanNuke/masks/fold3/masks.npy --type_path=datasets/PanNuke/masks/fold3/types.npy \
--pred_path=$WEIGHT_BASE_PATH/PanNukeCocoDataset/preds_pannuke.npy --save_path=$WEIGHT_BASE_PATH
```

## 👉 Infer

Our trained checkpoint can be downloaded from the `models` folder in the [Google Drive](https://drive.google.com/drive/folders/1MezZrVwx7S6MNYkpMO5ja2D6KcZkRvYo?usp=sharing).

```shell script
# Segment image by image
CUDA_VISIBLE_DEVICES=0 python tools/infer.py demo/imgs configs/nuhtc/htc_lite_swin_pytorch_fpn_PanNuke_seasaw_CAS.py models/pannuke.pth --out demo/imgs_infer
```

## 🚀 Segment the Whole Slide Image

Run WSI segmentation and export results as `qupath`, `sql`, `dsa`, or `coco`. Magnification is not auto-selected—use the scale that matches your slides (default pipeline assumes 40×).

> Note: QuPath export includes both point and contour representations for nuclei. COCO is currently used only for per-patch segmentation outputs.

1. WSI Segmentation

```shell script
CUDA_VISIBLE_DEVICES=0 python tools/infer_wsi.py demo/wsi configs/nuhtc/htc_lite_swin_pytorch_fpn_PanNuke_seasaw_CAS.py models/pannuke.pth \
--patch --seg --stitch --patch_size 256 --step_size 192 --margin 1 --min_area 10 \
--batch_size 32 --save_dir demo/wsi_infer --mode qupath
```

2. Merge Overlapping Nuclei

After segmentation, mask non-maximum suppression (NMS) is applied to the WSI to remove the overlapping nuclei.

```shell script
# --geojson: path to the slide segmentation .geojson file (e.g. from infer_wsi: .../nuclei/<SLIDE_ID>/<SLIDE_ID>.geojson).
python tools/nuclei_merge.py \
--geojson demo/wsi_res/TCGA-AC-A2FK-01Z-00-DX1.033F3C27-9860-4EF3-9330-37DE5EC45724.geojson \
--overlap_threshold 0.05 --merge_strategy probability
```

We provide a WSI example from TCGA (filename: `TCGA-AC-A2FK-01Z-00-DX1.033F3C27-9860-4EF3-9330-37DE5EC45724.svs`), which includes the `geojson` file for both nuclei points and contours. These can be easily dragged into, viewed, and edited using [QuPath](https://qupath.github.io/) (checked in QuPath Version 0.5.1). The WSI example can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1UdCixl10kBxyKHUGOww4WIkdoFma-UIr?usp=drive_link).

The `dsa` is a format supported by [Digital Slide Archive](https://digitalslidearchive.github.io/digital_slide_archive/), a powerful containerized web-based platform for storing, managing, viewing, and analysing WSIs. If you are interested in using the DSA platform, please refer to its [deployment instructions](https://github.com/DigitalSlideArchive/digital_slide_archive/blob/master/devops/dsa/README.rst).

Our model is trained with a patch size `256×256` at 40× magnification. During inference, it maintains strong performance even when evaluated with a larger patch size of `512×512`. To run inference using `512×512` patches, please specify the arguments `--patch_size 512 --step_size 448`.

## 🔬 Extract the Nuclei Feature

Make sure you have successfully installed the [histomicstk](https://digitalslidearchive.github.io/HistomicsTK/).
We support two approaches for extracting nucleus features:

1. Extract a patch centered on each nucleus and then compute features individually (recommended).

2. Tile the WSI and extract the nucleus features from each tile sequentially.

We recommend using the first way. Here is an example:

```shell script
# demo/wsi is the path to the folder containing raw WSI image files
# segdir is the path to the folder containing segmentation files
python tools/wsi_feat_extract.py demo/wsi --segdir demo/wsi_res --mag 40
```

For the second way that tiles images first, please specify `--mode coco` or `--mode all` during WSI inference.

```shell script
python tools/nuclei_feat_extract.py demo/wsi_res
# datadir (str)
# Path to the folder containing raw WSI image files.

# --start (int, default: 0)
# Starting index of the slides to process. Useful for batching or parallel execution.
# --end (int, default: None)
# Ending index (exclusive) of the slides to process. If not specified, all remaining slides will be processed.
# --min_num (int, default: 8)
# Minimum number of nuclei required in a patch. Patches with fewer nuclei will be excluded.
# --patch_size (int, default: 512)
# Size (in pixels) of each image patch. Should match the expected input size (e.g., 256 or 512 for 40× resolution) used during inference.
# --reverse (flag, default: False)
# If specified, slide IDs will be processed in reverse order.
```

It will extract the nuclei feature for each image and then store them in a csv file. The following is an example for the nuclei feature csv file.

|     | Label | Identifier.Xmin | Identifier.Ymin | Identifier.Xmax | Identifier.Ymax | Identifier.CentroidX | Identifier.CentroidY | Identifier.WeightedCentroidX | Identifier.WeightedCentroidY | Orientation.Orientation | Size.Area | Size.ConvexHullArea | Size.MajorAxisLength | Size.MinorAxisLength | Size.Perimeter | Shape.Circularity | Shape.Eccentricity | Shape.EquivalentDiameter | Shape.Extent | Shape.FractalDimension | Shape.MinorMajorAxisRatio | Shape.Solidity | Shape.HuMoments1 | Shape.HuMoments2 | Shape.HuMoments3 | Shape.HuMoments4 | Shape.HuMoments5 | Shape.HuMoments6 | Shape.HuMoments7 | Shape.WeightedHuMoments1 | Shape.WeightedHuMoments2 | Shape.WeightedHuMoments3 | Shape.WeightedHuMoments4 | Shape.WeightedHuMoments5 | Shape.WeightedHuMoments6 | Shape.WeightedHuMoments7 | Shape.FSD1 | Shape.FSD2 | Shape.FSD3 | Shape.FSD4 | Shape.FSD5 | Shape.FSD6 | Nucleus.Intensity.Min | Nucleus.Intensity.Max | Nucleus.Intensity.Mean | Nucleus.Intensity.Median | Nucleus.Intensity.MeanMedianDiff | Nucleus.Intensity.Std | Nucleus.Intensity.IQR | Nucleus.Intensity.MAD | Nucleus.Intensity.Skewness | Nucleus.Intensity.Kurtosis | Nucleus.Intensity.HistEnergy | Nucleus.Intensity.HistEntropy | Nucleus.Gradient.Mag.Mean | Nucleus.Gradient.Mag.Std | Nucleus.Gradient.Mag.Skewness | Nucleus.Gradient.Mag.Kurtosis | Nucleus.Gradient.Mag.HistEntropy | Nucleus.Gradient.Mag.HistEnergy | Nucleus.Gradient.Canny.Sum | Nucleus.Gradient.Canny.Mean | Nucleus.Haralick.ASM.Mean | Nucleus.Haralick.ASM.Range | Nucleus.Haralick.Contrast.Mean | Nucleus.Haralick.Contrast.Range | Nucleus.Haralick.Correlation.Mean | Nucleus.Haralick.Correlation.Range | Nucleus.Haralick.SumOfSquares.Mean | Nucleus.Haralick.SumOfSquares.Range | Nucleus.Haralick.IDM.Mean | Nucleus.Haralick.IDM.Range | Nucleus.Haralick.SumAverage.Mean | Nucleus.Haralick.SumAverage.Range | Nucleus.Haralick.SumVariance.Mean | Nucleus.Haralick.SumVariance.Range | Nucleus.Haralick.SumEntropy.Mean | Nucleus.Haralick.SumEntropy.Range | Nucleus.Haralick.Entropy.Mean | Nucleus.Haralick.Entropy.Range | Nucleus.Haralick.DifferenceVariance.Mean | Nucleus.Haralick.DifferenceVariance.Range | Nucleus.Haralick.DifferenceEntropy.Mean | Nucleus.Haralick.DifferenceEntropy.Range | Nucleus.Haralick.IMC1.Mean | Nucleus.Haralick.IMC1.Range | Nucleus.Haralick.IMC2.Mean | Nucleus.Haralick.IMC2.Range | cell_type | img_id | img_type | img_objs | file_name |
|-----|-------|-----------------|-----------------|-----------------|-----------------|----------------------|----------------------|------------------------------|------------------------------|-------------------------|-----------|---------------------|----------------------|----------------------|----------------|-------------------|--------------------|--------------------------|--------------|------------------------|---------------------------|----------------|------------------|------------------|------------------|------------------|------------------|------------------|------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|------------|------------|------------|------------|------------|------------|-----------------------|-----------------------|------------------------|--------------------------|----------------------------------|-----------------------|-----------------------|-----------------------|----------------------------|----------------------------|------------------------------|-------------------------------|---------------------------|--------------------------|-------------------------------|-------------------------------|----------------------------------|---------------------------------|----------------------------|-----------------------------|---------------------------|----------------------------|--------------------------------|---------------------------------|-----------------------------------|------------------------------------|------------------------------------|-------------------------------------|---------------------------|----------------------------|----------------------------------|-----------------------------------|-----------------------------------|------------------------------------|----------------------------------|-----------------------------------|-------------------------------|--------------------------------|------------------------------------------|-------------------------------------------|-----------------------------------------|------------------------------------------|----------------------------|-----------------------------|----------------------------|-----------------------------|-----------|--------|----------|----------|-----------|
| 211 | 1     | 322             | 402             | 356             | 442             | 338.111              | 421.776              | 337.964                      | 422.038                      | 0.319                   | 1028.000  | 1079.000            | 40.400               | 32.539               | 131.698        | 0.745             | 0.593              | 36.179                   | 0.756        | 0.851                  | 0.805                     | 0.953          | 0.164            | 0.001            | 0.000            | 0.000            | -0.000           | -0.000           | -0.000           | 0.001                    | 0.000                    | 0.000                    | 0.000                    | 0.000                    | -0.000                   | 0.000                    | 0.231      | 0.003      | 0.004      | 0.006      | 0.119      | 0.307      | 94.000                | 251.000               | 210.930                | 214.000                  | -3.070                           | 21.928                | 26.000                | 13.000                | -1.193                     | 2.762                      | 0.224                        | 1.677                         | 10.849                    | 10.623                   | 1.933                         | 3.910                         | 1.394                            | 0.340                           | 139.000                    | 0.135                       | 0.021                     | 0.005                      | 4.897                          | 4.211                           | 0.950                             | 0.045                              | 49.706                             | 2.771                               | 0.563                     | 0.080                      | 44.098                           | 0.556                             | 193.929                           | 15.296                             | 5.182                            | 0.058                             | 6.760                         | 0.383                          | 0.008                                    | 0.002                                     | 2.252                                   | 0.432                                    | -0.399                     | 0.107                       | 0.982                      | 0.017                       | E         | 28     | C        | 32       | 28.png    |
| 212 | 2     | 260             | 464             | 294             | 506             | 276.493              | 484.931              | 276.498                      | 484.768                      | 0.274                   | 1076.000  | 1141.000            | 42.793               | 32.271               | 136.284        | 0.728             | 0.657              | 37.014                   | 0.754        | 0.921                  | 0.754                     | 0.943          | 0.167            | 0.002            | 0.000            | 0.000            | 0.000            | 0.000            | 0.000            | 0.001                    | 0.000                    | 0.000                    | 0.000                    | -0.000                   | -0.000                   | -0.000                   | 0.391      | 0.002      | 0.005      | 0.010      | 0.040      | 0.270      | 99.000                | 252.000               | 218.624                | 223.500                  | -4.876                           | 21.567                | 21.000                | 10.500                | -1.869                     | 5.051                      | 0.261                        | 1.579                         | 11.734                    | 13.013                   | 1.946                         | 3.175                         | 1.493                            | 0.332                           | 179.000                    | 0.166                       | 0.028                     | 0.013                      | 5.475                          | 6.951                           | 0.946                             | 0.070                              | 51.300                             | 2.937                               | 0.552                     | 0.173                      | 46.675                           | 0.499                             | 199.723                           | 16.808                             | 4.970                            | 0.052                             | 6.488                         | 0.596                          | 0.007                                    | 0.003                                     | 2.302                                   | 0.703                                    | -0.381                     | 0.166                       | 0.974                      | 0.032                       | E         | 28     | C        | 32       | 28.png    |
| 213 | 3     | 292             | 446             | 328             | 486             | 309.592              | 465.089              | 309.533                      | 465.186                      | -0.464                  | 1128.000  | 1186.000            | 42.413               | 34.066               | 136.870        | 0.757             | 0.596              | 37.897                   | 0.783        | 0.805                  | 0.803                     | 0.951          | 0.164            | 0.001            | 0.000            | 0.000            | -0.000           | -0.000           | 0.000            | 0.001                    | 0.000                    | 0.000                    | 0.000                    | -0.000                   | -0.000                   | -0.000                   | 0.464      | 0.001      | 0.004      | 0.009      | 0.032      | 0.232      | 75.000                | 249.000               | 206.606                | 215.000                  | -8.394                           | 27.996                | 33.000                | 14.000                | -1.380                     | 2.014                      | 0.226                        | 1.728                         | 11.476                    | 10.901                   | 1.674                         | 2.224                         | 1.655                            | 0.262                           | 165.000                    | 0.146                       | 0.025                     | 0.005                      | 3.773                          | 2.798                           | 0.949                             | 0.040                              | 37.297                             | 2.146                               | 0.568                     | 0.101                      | 45.545                           | 0.499                             | 145.417                           | 11.384                             | 5.082                            | 0.050                             | 6.550                         | 0.338                          | 0.008                                    | 0.002                                     | 2.147                                   | 0.414                                    | -0.408                     | 0.100                       | 0.982                      | 0.015                       | E         | 28     | C        | 32       | 28.png    |
| 214 | 4     | 328             | 448             | 360             | 486             | 342.695              | 466.659              | 342.382                      | 466.577                      | -0.532                  | 904.000   | 954.000             | 40.653               | 28.492               | 124.870        | 0.729             | 0.713              | 33.927                   | 0.743        | 1.074                  | 0.701                     | 0.948          | 0.170            | 0.003            | 0.000            | 0.000            | 0.000            | 0.000            | 0.000            | 0.001                    | 0.000                    | 0.000                    | 0.000                    | -0.000                   | -0.000                   | 0.000                    | 0.420      | 0.000      | 0.002      | 0.006      | 0.138      | 0.202      | 101.000               | 245.000               | 185.750                | 184.000                  | 1.750                            | 25.644                | 30.000                | 15.000                | -0.014                     | -0.012                     | 0.165                        | 1.969                         | 9.395                     | 5.312                    | 0.851                         | 0.642                         | 2.006                            | 0.154                           | 150.000                    | 0.166                       | 0.017                     | 0.006                      | 1.786                          | 1.359                           | 0.963                             | 0.030                              | 24.275                             | 1.308                               | 0.597                     | 0.142                      | 40.420                           | 0.426                             | 95.315                            | 6.510                              | 5.173                            | 0.044                             | 6.450                         | 0.509                          | 0.010                                    | 0.003                                     | 1.754                                   | 0.396                                    | -0.464                     | 0.125                       | 0.989                      | 0.012                       | E         | 28     | C        | 32       | 28.png    |
| 215 | 5     | 266             | 404             | 316             | 454             | 290.478              | 427.398              | 290.924                      | 427.688                      | -0.785                  | 1808.000  | 1892.000            | 60.816               | 38.141               | 180.770        | 0.695             | 0.779              | 47.979                   | 0.723        | 0.826                  | 0.627                     | 0.956          | 0.178            | 0.006            | 0.000            | 0.000            | 0.000            | 0.000            | -0.000           | 0.001                    | 0.000                    | 0.000                    | 0.000                    | 0.000                    | 0.000                    | -0.000                   | 0.508      | 0.005      | 0.009      | 0.012      | 0.009      | 0.212      | 70.000                | 255.000               | 196.299                | 201.000                  | -4.701                           | 30.024                | 36.000                | 18.000                | -0.886                     | 1.016                      | 0.189                        | 1.841                         | 12.020                    | 10.098                   | 2.042                         | 4.591                         | 1.457                            | 0.298                           | 346.000                    | 0.191                       | 0.012                     | 0.004                      | 4.300                          | 4.445                           | 0.952                             | 0.051                              | 44.963                             | 1.119                               | 0.530                     | 0.129                      | 41.760                           | 0.324                             | 175.550                           | 8.610                              | 5.444                            | 0.032                             | 7.176                         | 0.562                          | 0.008                                    | 0.003                                     | 2.204                                   | 0.547                                    | -0.393                     | 0.131                       | 0.984                      | 0.020                       | E         | 28     | C        | 32       | 28.png    |

## 🗓️ Ongoing

- [x] Merge overlapping nuclei during WSI segmentation
- [x] Support nuclei feature extraction from WSIs
- [x] Add support for Docker

## 📖 Citation

```
@article{li2025nuhtc,
  title={NuHTC: A hybrid task cascade for nuclei instance segmentation and classification},
  author={Li, Bao and Liu, Zhenyu and Zhang, Song and Liu, Xiangyu and Sun, Caixia and Liu, Jiangang and Qiu, Bensheng and Tian, Jie},
  journal={Medical Image Analysis},
  volume={103},
  pages={103595},
  year={2025},
  publisher={Elsevier}
}
```

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