Add ultrasound confidence map to transforms

docs/requirements.txt

@@ -6,6 +6,7 @@ itk>=5.2
 nibabel
 parameterized
 scikit-image>=0.19.0
+scipy>=1.7.1
 tensorboard
 commonmark==0.9.1
 recommonmark==0.6.0

docs/source/installation.md

@@ -10,8 +10,8 @@
 		- [Uninstall the packages](#uninstall-the-packages)
 	- [From conda-forge](#from-conda-forge)
 	- [From GitHub](#from-github)
-		- [Option 1 (as a part of your system-wide module)](#option-1-as-a-part-of-your-system-wide-module)
-		- [Option 2 (editable installation)](#option-2-editable-installation)
+		- [Option 1 (as a part of your system-wide module):](#option-1-as-a-part-of-your-system-wide-module)
+		- [Option 2 (editable installation):](#option-2-editable-installation)
 	- [Validating the install](#validating-the-install)
 	- [MONAI version string](#monai-version-string)
 	- [From DockerHub](#from-dockerhub)
@@ -254,10 +254,10 @@ Since MONAI v0.2.0, the extras syntax such as `pip install 'monai[nibabel]'` is
 - The options are
 
 ```
-[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, zarr]
+[nibabel, skimage, scipy, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, zarr]
 ```
 
-which correspond to `nibabel`, `scikit-image`, `pillow`, `tensorboard`,
+which correspond to `nibabel`, `scikit-image`, `scipy`, `pillow`, `tensorboard`,
 `gdown`, `pytorch-ignite`, `torchvision`, `itk`, `tqdm`, `lmdb`, `psutil`, `cucim`, `openslide-python`, `pandas`, `einops`, `transformers`, `mlflow`, `clearml`, `matplotlib`, `tensorboardX`, `tifffile`, `imagecodecs`, `pyyaml`, `fire`, `jsonschema`, `ninja`, `pynrrd`, `pydicom`, `h5py`, `nni`, `optuna`, `onnx`, `onnxruntime`, and `zarr` respectively.
 
 - `pip install 'monai[all]'` installs all the optional dependencies.

monai/config/deviceconfig.py

@@ -71,6 +71,7 @@ def get_optional_config_values():
     output["ITK"] = get_package_version("itk")
     output["Nibabel"] = get_package_version("nibabel")
     output["scikit-image"] = get_package_version("skimage")
+    output["scipy"] = get_package_version("scipy")
     output["Pillow"] = get_package_version("PIL")
     output["Tensorboard"] = get_package_version("tensorboard")
     output["gdown"] = get_package_version("gdown")

monai/data/__init__.py

@@ -150,3 +150,5 @@ def reduce_meta_tensor(meta_tensor):
         return _rebuild_meta, (type(meta_tensor), storage, dtype, metadata)
 
     ForkingPickler.register(MetaTensor, reduce_meta_tensor)
+
+from .ultrasound_confidence_map import UltrasoundConfidenceMap

monai/data/ultrasound_confidence_map.py

@@ -0,0 +1,352 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import numpy as np
+from numpy.typing import NDArray
+
+from monai.utils import min_version, optional_import
+
+__all__ = ["UltrasoundConfidenceMap"]
+
+cv2, _ = optional_import("cv2")
+csc_matrix, _ = optional_import("scipy.sparse", "1.7.1", min_version, "csc_matrix")
+spsolve, _ = optional_import("scipy.sparse.linalg", "1.7.1", min_version, "spsolve")
+hilbert, _ = optional_import("scipy.signal", "1.7.1", min_version, "hilbert")
+
+
+class UltrasoundConfidenceMap:
+    """Compute confidence map from an ultrasound image.
+    This transform uses the method introduced by Karamalis et al. in https://doi.org/10.1016/j.media.2012.07.005.
+    It generates a confidence map by setting source and sink points in the image and computing the probability
+    for random walks to reach the source for each pixel.
+
+    Args:
+        alpha (float, optional): Alpha parameter. Defaults to 2.0.
+        beta (float, optional): Beta parameter. Defaults to 90.0.
+        gamma (float, optional): Gamma parameter. Defaults to 0.05.
+        mode (str, optional): 'RF' or 'B' mode data. Defaults to 'B'.
+        sink_mode (str, optional): Sink mode. Defaults to 'all'. If 'mask' is selected, a mask must be when calling
+            the transform. Can be 'all', 'mid', 'min', or 'mask'.
+    """
+
+    def __init__(self, alpha: float = 2.0, beta: float = 90.0, gamma: float = 0.05, mode="B", sink_mode="all"):
+        # The hyperparameters for confidence map estimation
+        self.alpha = alpha
+        self.beta = beta
+        self.gamma = gamma
+        self.mode = mode
+        self.sink_mode = sink_mode
+
+        # The precision to use for all computations
+        self.eps = np.finfo("float64").eps
+
+        # Store sink indices for external use
+        self._sink_indices = np.array([], dtype="float64")
+
+    def sub2ind(self, size: tuple[int, ...], rows: NDArray, cols: NDArray) -> NDArray:
+        """Converts row and column subscripts into linear indices,
+        basically the copy of the MATLAB function of the same name.
+        https://www.mathworks.com/help/matlab/ref/sub2ind.html
+
+        This function is Pythonic so the indices start at 0.
+
+        Args:
+            size Tuple[int]: Size of the matrix
+            rows (NDArray): Row indices
+            cols (NDArray): Column indices
+
+        Returns:
+            indices (NDArray): 1-D array of linear indices
+        """
+        indices: NDArray = rows + cols * size[0]
+        return indices
+
+    def get_seed_and_labels(
+        self, data: NDArray, sink_mode: str = "all", sink_mask: NDArray | None = None
+    ) -> tuple[NDArray, NDArray]:
+        """Get the seed and label arrays for the max-flow algorithm
+
+        Args:
+            data: Input array
+            sink_mode (str, optional): Sink mode. Defaults to 'all'.
+            sink_mask (NDArray, optional): Sink mask. Defaults to None.
+
+        Returns:
+            Tuple[NDArray, NDArray]: Seed and label arrays
+        """
+
+        # Seeds and labels (boundary conditions)
+        seeds = np.array([], dtype="float64")
+        labels = np.array([], dtype="float64")
+
+        # Indices for all columns
+        sc = np.arange(data.shape[1], dtype="float64")
+
+        # SOURCE ELEMENTS - 1st matrix row
+        # Indices for 1st row, it will be broadcasted with sc
+        sr_up = np.array([0])
+        seed = self.sub2ind(data.shape, sr_up, sc).astype("float64")
+        seed = np.unique(seed)
+        seeds = np.concatenate((seeds, seed))
+
+        # Label 1
+        label = np.ones_like(seed)
+        labels = np.concatenate((labels, label))
+
+        # Create seeds for sink elements
+
+        if sink_mode == "all":
+            # All elements in the last row
+            sr_down = np.ones_like(sc) * (data.shape[0] - 1)
+            self._sink_indices = np.array([sr_down, sc], dtype="int32")
+            seed = self.sub2ind(data.shape, sr_down, sc).astype("float64")
+
+        elif sink_mode == "mid":
+            # Middle element in the last row
+            sc_down = np.array([data.shape[1] // 2])
+            sr_down = np.ones_like(sc_down) * (data.shape[0] - 1)
+            self._sink_indices = np.array([sr_down, sc_down], dtype="int32")
+            seed = self.sub2ind(data.shape, sr_down, sc_down).astype("float64")
+
+        elif sink_mode == "min":
+            # Minimum element in the last row (excluding 10% from the edges)
+            ten_percent = int(data.shape[1] * 0.1)
+            min_val = np.min(data[-1, ten_percent:-ten_percent])
+            min_idxs = np.where(data[-1, ten_percent:-ten_percent] == min_val)[0] + ten_percent
+            sc_down = min_idxs
+            sr_down = np.ones_like(sc_down) * (data.shape[0] - 1)
+            self._sink_indices = np.array([sr_down, sc_down], dtype="int32")
+            seed = self.sub2ind(data.shape, sr_down, sc_down).astype("float64")
+
+        elif sink_mode == "mask":
+            # All elements in the mask
+            coords = np.where(sink_mask != 0)
+            sr_down = coords[0]
+            sc_down = coords[1]
+            self._sink_indices = np.array([sr_down, sc_down], dtype="int32")
+            seed = self.sub2ind(data.shape, sr_down, sc_down).astype("float64")
+
+        seed = np.unique(seed)
+        seeds = np.concatenate((seeds, seed))
+
+        # Label 2
+        label = np.ones_like(seed) * 2
+        labels = np.concatenate((labels, label))
+
+        return seeds, labels
+
+    def normalize(self, inp: NDArray) -> NDArray:
+        """Normalize an array to [0, 1]"""
+        normalized_array: NDArray = (inp - np.min(inp)) / (np.ptp(inp) + self.eps)
+        return normalized_array
+
+    def attenuation_weighting(self, img: NDArray, alpha: float) -> NDArray:
+        """Compute attenuation weighting
+
+        Args:
+            img (NDArray): Image
+            alpha: Attenuation coefficient (see publication)
+
+        Returns:
+            w (NDArray): Weighting expressing depth-dependent attenuation
+        """
+
+        # Create depth vector and repeat it for each column
+        dw = np.linspace(0, 1, img.shape[0], dtype="float64")
+        dw = np.tile(dw.reshape(-1, 1), (1, img.shape[1]))
+
+        w: NDArray = 1.0 - np.exp(-alpha * dw)  # Compute exp inline
+
+        return w
+
+    def confidence_laplacian(self, padded_index: NDArray, padded_image: NDArray, beta: float, gamma: float):
+        """Compute 6-Connected Laplacian for confidence estimation problem
+
+        Args:
+            padded_index (NDArray): The index matrix of the image with boundary padding.
+            padded_image (NDArray): The padded image.
+            beta (float): Random walks parameter that defines the sensitivity of the Gaussian weighting function.
+            gamma (float): Horizontal penalty factor that adjusts the weight of horizontal edges in the Laplacian.
+
+        Returns:
+            L (csc_matrix): The 6-connected Laplacian matrix used for confidence map estimation.
+        """
+
+        m, _ = padded_index.shape
+
+        padded_index = padded_index.T.flatten()
+        padded_image = padded_image.T.flatten()
+
+        p = np.where(padded_index > 0)[0]
+
+        i = padded_index[p] - 1  # Index vector
+        j = padded_index[p] - 1  # Index vector
+        # Entries vector, initially for diagonal
+        s = np.zeros_like(p, dtype="float64")
+
+        edge_templates = [
+            -1,  # Vertical edges
+            1,
+            m - 1,  # Diagonal edges
+            m + 1,
+            -m - 1,
+            -m + 1,
+            m,  # Horizontal edges
+            -m,
+        ]
+
+        vertical_end = None
+
+        for iter_idx, k in enumerate(edge_templates):
+            neigh_idxs = padded_index[p + k]
+
+            q = np.where(neigh_idxs > 0)[0]
+
+            ii = padded_index[p[q]] - 1
+            i = np.concatenate((i, ii))
+            jj = neigh_idxs[q] - 1
+            j = np.concatenate((j, jj))
+            w = np.abs(padded_image[p[ii]] - padded_image[p[jj]])  # Intensity derived weight
+            s = np.concatenate((s, w))
+
+            if iter_idx == 1:
+                vertical_end = s.shape[0]  # Vertical edges length
+            elif iter_idx == 5:
+                s.shape[0]  # Diagonal edges length
+
+        # Normalize weights
+        s = self.normalize(s)
+
+        # Horizontal penalty
+        s[:vertical_end] += gamma
+        # s[vertical_end:diagonal_end] += gamma * np.sqrt(2) # --> In the paper it is sqrt(2)
+        # since the diagonal edges are longer yet does not exist in the original code
+
+        # Normalize differences
+        s = self.normalize(s)
+
+        # Gaussian weighting function
+        s = -(
+            (np.exp(-beta * s, dtype="float64")) + 1.0e-6
+        )  # --> This epsilon changes results drastically default: 1.e-6
+
+        # Create Laplacian, diagonal missing
+        lap = csc_matrix((s, (i, j)))
+
+        # Reset diagonal weights to zero for summing
+        # up the weighted edge degree in the next step
+        lap.setdiag(0)
+
+        # Weighted edge degree
+        diag = np.abs(lap.sum(axis=0).A)[0]
+
+        # Finalize Laplacian by completing the diagonal
+        lap.setdiag(diag)
+
+        return lap
+
+    def _solve_linear_system(self, lap, rhs):
+        x = spsolve(lap, rhs)
+
+        return x
+
+    def confidence_estimation(self, img, seeds, labels, beta, gamma):
+        """Compute confidence map
+
+        Args:
+            img (NDArray): Processed image.
+            seeds (NDArray): Seeds for the random walks framework. These are indices of the source and sink nodes.
+            labels (NDArray): Labels for the random walks framework. These represent the classes or groups of the seeds.
+            beta: Random walks parameter that defines the sensitivity of the Gaussian weighting function.
+            gamma: Horizontal penalty factor that adjusts the weight of horizontal edges in the Laplacian.
+
+        Returns:
+            map: Confidence map which shows the probability of each pixel belonging to the source or sink group.
+        """
+
+        # Index matrix with boundary padding
+        idx = np.arange(1, img.shape[0] * img.shape[1] + 1).reshape(img.shape[1], img.shape[0]).T
+        pad = 1
+
+        padded_idx = np.pad(idx, (pad, pad), "constant", constant_values=(0, 0))
+        padded_img = np.pad(img, (pad, pad), "constant", constant_values=(0, 0))
+
+        # Laplacian
+        lap = self.confidence_laplacian(padded_idx, padded_img, beta, gamma)
+
+        # Select marked columns from Laplacian to create L_M and B^T
+        b = lap[:, seeds]
+
+        # Select marked nodes to create B^T
+        n = np.sum(padded_idx > 0).item()
+        i_u = np.setdiff1d(np.arange(n), seeds.astype(int))  # Index of unmarked nodes
+        b = b[i_u, :]
+
+        # Remove marked nodes from Laplacian by deleting rows and cols
+        keep_indices = np.setdiff1d(np.arange(lap.shape[0]), seeds)
+        lap = csc_matrix(lap[keep_indices, :][:, keep_indices])
+
+        # Define M matrix
+        m = np.zeros((seeds.shape[0], 1), dtype="float64")
+        m[:, 0] = labels == 1
+
+        # Right-handside (-B^T*M)
+        rhs = -b @ m  # type: ignore
+
+        # Solve linear system
+        x = self._solve_linear_system(lap, rhs)
+
+        # Prepare output
+        probabilities = np.zeros((n,), dtype="float64")
+        # Probabilities for unmarked nodes
+        probabilities[i_u] = x
+        # Max probability for marked node
+        probabilities[seeds[labels == 1].astype(int)] = 1.0
+
+        # Final reshape with same size as input image (no padding)
+        probabilities = probabilities.reshape((img.shape[1], img.shape[0])).T
+
+        return probabilities
+
+    def __call__(self, data: NDArray, sink_mask: NDArray | None = None) -> NDArray:
+        """Compute the confidence map
+
+        Args:
+            data (NDArray): RF ultrasound data (one scanline per column)
+
+        Returns:
+            map (NDArray): Confidence map
+        """
+
+        # Normalize data
+        data = data.astype("float64")
+        data = self.normalize(data)
+
+        if self.mode == "RF":
+            # MATLAB hilbert applies the Hilbert transform to columns
+            data = np.abs(hilbert(data, axis=0)).astype("float64")  # type: ignore
+
+        seeds, labels = self.get_seed_and_labels(data, self.sink_mode, sink_mask)
+
+        # Attenuation with Beer-Lambert
+        w = self.attenuation_weighting(data, self.alpha)
+
+        # Apply weighting directly to image
+        # Same as applying it individually during the formation of the
+        # Laplacian
+        data = data * w
+
+        # Find condidence values
+        map_: NDArray = self.confidence_estimation(data, seeds, labels, self.beta, self.gamma)
+
+        return map_