AUTONOMOUS SEGMENTATION OF CONTRAST FILLED CORONARY ARTERY VESSELS ON COMPUTED TOMOGRAPHY IMAGES

A computer-implemented approach produces a patient-specific three-dimensional (3-D) surface mesh of contrast-enhanced coronary-artery vessels directly from computed-tomography (CT) data. A CT volume is windowed and intensity-normalized; a three-dimensional Jerman vesselness filter is then applied. The normalized CT data and vesselness response form separate channels of a multi-channel volume. A first three-dimensional convolutional neural network (CNN) delineates the pericardium, and morphological dilation of that mask defines a safety margin limiting subsequent analysis to the cardiac region. The masked multi-channel volume is subdivided, and a second 3-D CNN concurrently analyzes both channels to predict coronary-vessel probability maps that are reassembled into a whole-volume binary vessel mask. Small disconnected components are discarded and the mask is morphologically smoothed. Finally, a triangulation stage converts the refined mask into a surface mesh suitable for visualization or quantitative analysis. Corresponding systems and non-transitory media store instructions and pretrained network weights.

TECHNICAL FIELD

The invention generally relates to autonomous segmentation of contrast filled coronary artery vessels on computed tomography images, useful in particular for the field of computer assisted diagnosis, treatment, and monitoring of coronary artery diseases.

BACKGROUND

Specialized computer systems can be used to process the CT images to develop three-dimensional models of the anatomy fragments. For this purpose, various machine learning technologies are developed, such as a convolutional neural network (CNN) that is a class of deep, feed-forward artificial neural networks. CNNs use a variation of feature detectors and/or multilayer perceptrons designed to require minimal preprocessing of input data.

SUMMARY OF THE INVENTION

So far, the image processing systems were not capable of efficiently providing autonomous segmentation of contrast filled coronary artery vessels on CT images and, therefore, Applicant has recognized a need to provide improvements in this area.

Certain embodiments disclosed herein relate to machine learning based detection of vascular structures in medical images, and more particularly, to machine learning based detection of coronary vessels in computed tomography (CT) images. Automatic detection and segmentation of contrast filled coronary arteries CT scans facilitates the diagnosis, treatment, and monitoring of coronary artery diseases.

In one aspect, the invention relates to a computer-implemented method of generating a patient-specific three-dimensional surface mesh of contrast-filled coronary-artery vessels, the method comprising:

In some embodiments, computing the Jerman filter response in step (c) comprises computing the three-dimensional Jerman filter response across the entire normalized CT volume.

In some embodiments, the pre-processing of step (b) comprises one or more of windowing, intensity normalization, and filtering.

In some embodiments, the first convolutional neural network is trained with a loss function that includes dice loss, Tversky loss, or a combination thereof.

In some embodiments, wherein the spherical structuring element used in step (f) has a radius of 2-5 voxels.

In some embodiments, the sub-volumes processed in steps (e) and (h) overlap with one another.

In some embodiments, the second convolutional neural network comprises an input layer configured to accept two channels respectively corresponding to the normalized CT data and the vesselness data.

In some embodiments, the second convolutional neural network employs three-dimensional convolutional layers in its encoder.

In some embodiments, the post-processing of step (i) removes connected components having fewer than a user-defined voxel-count threshold.

In some embodiments, converting the post-processed coronary-vessel mask to the triangulated surface mesh in step (j) comprises generating the surface mesh in a file format configured for three-dimensional visualization or downstream analysis.

In another aspect, the invention relates to a computer system comprising at least one processor and at least one non-transitory memory storing instructions that, when executed by the processor, perform the method as described herein.

In some embodiments, the instructions schedule neural-network inference on one or more processors or hardware accelerators.

In some embodiments, the non-transitory memory further stores pre-trained weights for both the first and second convolutional neural networks.

In another aspect, the invention relates to a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the processors to perform the method of as described herein.

In some embodiments, the instructions include program code to conduct cosine-annealing learning-rate scheduling with early stopping when training one or both of the convolutional neural networks.

In another aspect, the invention relates to a computer-implemented method of preparing a CT volume for coronary-vessel segmentation, the method comprising:

In some embodiments, the spherical structuring element has a radius of 3 voxels.

In some embodiments, the method further comprises computing a three-dimensional Jerman filter response of the CT scan, masking the response with the expanded mask, and supplying both the masked CT volume and the masked filter response as separate channels to a coronary-vessel segmentation neural network.

In another aspect, the invention relates to a computer system comprising at least one processor and at least one non-transitory memory storing instructions that, when executed by the processor, perform the method as described herein.

In another aspect, the invention relates to a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the processors to perform the method as described herein.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The description is not to be taken in a limiting sense, but is made merely for the purpose of illustrating the general principles of the invention.

The overview of a segmentation method, including a first embodiment (with steps 103 A, 104 A, 106 A, 107 A) and a second embodiment (with steps 103 B, 104 B, 106 B, 107 B) is presented in detail in FIG. 1. In step 101, a computer tomography (CT) volumetric scan (also called a three-dimensional (3D) scan or a volume) is received. The CT volume comprises a set of medical scan images of a region of the anatomy, such as a set of DICOM (Digital Imaging and Communications in Medicine) images. The set 201 represents consecutive slices of the region of the anatomy, such as illustrated in FIG. 2A.

In some embodiments the method is integrated into a clinical workflow that automatically queries a Picture Archiving and Communication System (PACS) for the patient's contrast-enhanced cardiac CT series, runs the segmentation pipeline described herein, and returns both the binary mask and the resulting surface mesh to the PACS or an electronic-medical-record (EMR) system as secondary-capture DICOM objects or as report attachments. This end-to-end automation allows the coronary-vessel model to be available to the interpreting physician within minutes of image acquisition.

The region of the anatomy should be selected such that it contains the heart and the coronary arteries 202, such as shown in FIG. 2B.

In the embodiments described herein, at least some of the computational steps, such as pre-processing, filter evaluation, region-of-interest extraction, vessel segmentation, and post-processing, are preferably performed at the native voxel resolution recorded by the CT scanner. No intermediate resampling, anisotropic scaling, or resolution change is applied unless explicitly stated otherwise. Operating at acquisition-native resolution preserves quantitative Hounsfield values and avoids partial-volume artefacts that could degrade segmentation accuracy.

In step 102, the 3D volume is autonomously preprocessed to prepare the images for region of interest (ROI) extraction. This preprocessing step may comprise raw 3D CT data windowing, filtering and normalization, as well as computing the 3D Jerman filter response for the whole volume. Computing the Jerman filter can be performed in accordance with the article “Enhancement of Vascular Structures in 3D and 2D Angiographic Images” (by T. Jerman, et al., IEEE Transactions on Medical Imaging, 35(9), p. 2107-2118 (2016)). The Jerman filter emphasizes elongated structures in images and volumes. An example of applying the filter on infrared hand vessel pattern image (left) 203 is shown in FIG. 2C, wherein the right image 204 shows the output, processed image.

The three-dimensional Jerman filter, which can be called a Jerman vesselness filter, may be evaluated at a plurality of Gaussian scales (e.g., σ=1 voxel, 2 voxels, and 3 voxels) to enhance vessels of different calibre. The scale-specific responses are then combined voxel-wise, for example by maximum-intensity projection, to form a single, scale-invariant vesselness volume.

Although the first embodiment illustrates ROI (pericardium) extraction from single-channel CT data, in alternative embodiments the ROI extraction CNN may receive multiple input channels. For example, a two-channel arrangement can be employed in which first channel carries the normalized CT slice (or sub-volume) and second channel carries the corresponding slice (or sub-volume) of the Jerman vesselness response. The CNN thereby learns to exploit both raw-intensity and vessel-enhancement cues when delineating the pericardium. The network architecture, loss functions, and training regimen remain as described above, with the only modification being the expanded number of input feature maps.

Next, in accordance with a first embodiment of the segmentation procedure, in step 103 A the 3D volume is converted to 3 sets of two-dimensional (2D) slices, wherein the first set is arranged along the axial plane, the second set is arranged along the sagittal plane and the third set is arranged along the coronal plane (as marked in FIG. 2A). Next, in step 104 A a region of interest (ROI) is extracted by autonomous segmentation of the heart region as outlined by the pericardium. The procedure is performed by three individually trained convolutional neural networks (CNNs), each for processing a particular one of the three sets of 2D slices, namely an axial plane ROI extraction CNN, a sagittal plane ROI extraction CNN and a coronal plano ROI extraction CNN. These three CNNs are trained by training data that consists of pairs of CT volume slices in its corresponding plane and its corresponding binary, expert-annotated mask, denoting the heart region as delineated by the pericardium. Direct correspondence of binary masks and CT scan data enables their direct use for segmentation training. Sample annotations 205, 206, 207 and desired results 208, 209, 210 for the three imaging planes for two different slices in each plane are shown in FIG. 2D. The training procedure for all the three networks is identical, though each one uses a different set of data. A part of the training set is held out as a validation set.

A schematic representation of the ROI extraction CNN in accordance with one embodiment is shown in FIG. 4. It will be described herein for use with the first embodiment of the segmentation method, while modifications for use in the second embodiment will be described later on. The input data represents a CT volume slice in a particular plane. The left side of the network is the encoder 401, which is a convolutional neural network, and the right side is the decoder 402. The encoder 401 may include a number of convolutional layers and a number of pooling layers, each pooling layer preceded by at least one convolutional layer. The encoder might be either pretrained, or trained from random initialisation. The decoder 402 path may include a number of convolutional layers and a number of upsampling layers, each upsampling layer preceded by at least one convolutional layer, and may include a transpose convolution operation which performs upsampling and interpolation with a learned kernel. The network may include a number of residual connections bypassing groups of layers in both the encoder and the decoder.

The residual connections may be either unit residual connections, or residual connections with trainable parameters. The residual connections can bypass one or more layers. Furthermore, there can be more than one residual connection in a section of the network. The network may include a number of skip connections connecting the encoder and the decoder section. The skip connections may be either unit connections or connections with trainable parameters. Skip connections improve the performance through information merging enabling the use of information from the encoder stages to train the deconvolution filters to upsample. The number of layers and number of filters within a layer is also subject to change, depending on the requirements of the application. The final layer for segmentation outputs a mask denoting the heart region as delineated by the pericardium (such as shown in FIG. 2D)—for example, it can be a binary mask.

The convolution layers can be of a standard kind, the dilated kind, or a combination thereof, with RcLU, leaky ReLU, Swish or Mish activation attached.

The upsampling or deconvolution layers can be of a standard kind, the dilated kind, or a combination thereof, with ReLU, leaky ReLU, Swish or Mish activation attached.

During training, the network may repeatedly perform the following steps:

In further embodiments the encoder is pre-trained on large, unlabelled CT datasets using self-supervised contrastive learning or federated learning conducted across multiple institutions, after which the network is fine-tuned on labelled coronary-artery data.

Doing so, the network adjusts its parameters and improves its predictions over time. During training, the following means of improving the training accuracy can be used:

To balance region-based and boundary-based learning signals the loss may be a weighted sum of Dice loss and Tversky loss, L=α·LDice+(1−α)·LTversky, with α typically set between 0.3 and 0.7 (e.g., α=0.5).

The training process may include periodic check of the prediction accuracy using a held out input data set (the validation set) not included in the training data. If the check reveals that the accuracy on the validation set is better than the one achieved during the previous check, the complete neural network weights are stored for further use. The early stopping function may terminate the training if there is no improvement observed during the last CH checks. Otherwise, the training is terminated after a predefined number of steps S.

The training procedure may be performed according to the outline shown in FIG. 5 in accordance with one embodiment of the training procedure. The training starts at 501. At 502, batches of training images are read from the training set, one batch at a time.

At 503 the images can be augmented. Data augmentation is performed on these images to make the training set more diverse. The input/output data pair is subjected to the same combination of transformations from the following set: rotation, scaling, movement, horizontal flip, additive noise of Gaussian and/or Poisson distribution and Gaussian blur, elastic transform, brightness shift, contrast/gamma changes, grid/optical distortion, batch-level samples averaging, random dropout, etc.

At 504, the images and generated augmented images are then passed through the layers of the CNN in a standard forward pass. The forward pass returns the results, which are then used to calculate at 505 the value of the loss function—the difference between the desired output and the actual, computed output. The difference can be expressed using a similarity metric, e.g.: mean squared error, mean average error, categorical cross-entropy or another metric.

At 506, weights are updated as per the specified optimizer and optimizer learning rate. The loss may be calculated using a per-pixel cross-entropy loss function and the Adam update rule.

The loss is also back-propagated through the network, and the gradients are computed. Based on the gradient values, the network's weights are updated. The process (beginning with the image batch read) is repeated continuously until an end of the training session is reached at 507.

Then, at 508, the performance metrics are calculated using a validation dataset—which is not explicitly used in training set. This is done in order to check at 509 whether not the model has improved. If it isn't the case, the early stop counter is incremented at 514 and it is checked at 515 if its value has reached a predefined number of epochs. If so, then the training process is complete at 516, since the model hasn't improved for many sessions now, so it can be concluded that the network started overfitting to the training data.

If the model has improved, the model is saved at 510 for further use and the early stop counter is reset at 511. As the final step in a session, learning rate scheduling can be applied. The session at which the rate is to be changed are predefined. Once one of the session numbers is reached at 512, the learning rate is set to one associated with this specific session number at 513.

For quality assurance the trained network can be executed multiple times with Monte-Carlo dropout enabled, producing an ensemble of probability maps whose per-voxel variance represents segmentation uncertainty. Voxels exceeding a predefined uncertainty threshold can be highlighted for human review.

Once the training is complete, the network can be used for inference, i.e. utilizing a trained model for prediction on new input data.

Upon the completion of the training, the weights of the neural network are stored and can be used for prediction. The input data for the prediction process are CT scan data of the heart volume with contrast filled coronary arteries. For prediction of the location of the heart in individual slices in the form of the binary mask, the data is propagated through all the layers of the networks, successively, until it reaches the final layer. The output of the final layer is a binary image containing the location of the heart as delineated by the pericardium.

The individual prediction of each neural network is an image. As the networks make predictions in a slice by slice manner, the volumetric information can be reconstructed simply by combining the predictions by stacking the slices.

The volumetric predictions in the 3 axes are then combined by averaging the individual results (e.g. calculating a sum of the components divided by the number of components) and applying a threshold and postprocessing by nonlinear filtering (morphological, median). The final result in 3D looks as shown below (a few different samples), as shown in FIG. 2D.

Next, in step 105, the preprocessed scan volume (as in FIG. 2B) and its corresponding 3d Jerman filter response (as in FIG. 2C) are additionally masked with the volumetric pericardium segmentation mask (as in FIG. 2D) with the addition of a safety margin (equal to e.g. a few voxels, preferably 3 voxels), so that all the information outside the pericardium region is cancelled out, wherein the result is a masked volume. For example, the pericardium can be postprocessed with dilation using ball-shaped kernel (radius=3 voxels). This is essential, since coronary arteries occupy only a part of the overall volume. Constraining the overall volume registered by the CT scanner to the subvolume that actually contains the heart improves the accuracy of the next step (the coronary vessel segmentation).

The radius R of the spherical structuring element used for the dilation step may be selected as a function of scanner resolution and desired safety margin. Suitable values lie between two and five voxels, inclusive; empirical testing shows that R=3 voxels offers a robust trade-off between complete vessel coverage and computational efficiency for most 0.5-0.8 mm isotropic cardiac CT protocols.

Both the masked raw volume (as shown in FIG. 2E—the top image 211 represents the 3D mask shape and the bottom image 212 represents the masked raw volume) and (optionally) the corresponding masked volumetric Jerman filter response 213 (as shown in FIG. 2F) are used as the input data for coronary vessel segmentation by the segmentation CNN. If the Jerman filter output is used, it is included as an additional channel of the input images.

In step 106 A, the masked volume is converted to three groups of two-dimensional slices, wherein each groups corresponds to a particular principal plane (the axial plane, the sagittal plane and the coronal plane) and the sets within the group correspond to planes tilted at an angle with respect to the principal plane.

FIG. 6A shows three such planes 601, 602, 603, one typical, aligned with the wall of the volume (i.e. along a principal plane), and two or more additional planes, each tilted at a small angle. For example, if 2N+1 planes are used, then the angles may be +/−(N*10)—which results in for example:

Such a set of planes moves along an axis of each principal plane instead of just one, as shown in FIG. 6A. The slanted planes are projected on a plane 604 analogous to the regular plane as shown in FIG. 6B. Therefore, three groups of sets of two-dimensional slices are obtained.

Next, in step 107 A, the coronary vessel segmentation is performed, preferably individually for each plane, by segmentation CNNs in a similar way as for pericardium, for the two-dimensional slices obtained in the previous step. Therefore, preferably (2N+1)*3 networks are used. All the networks share the same input size and the masks do not degrade due to interpolation, since Bresenham discretization pattern is used for sampling.

Prior to use, each of the neural networks used for prediction needs to be trained. The training data consists of pairs of CT volume slices in its corresponding plane and their corresponding binary, expert-annotated mask, denoting the coronary vessels. Direct correspondence of binary masks and CT scan data enables their direct use for segmentation training.

Sample annotation and desired result 605, 606, 607 for three imaging planes in each plane are shown in FIG. 6C—the left column 605 corresponds to the axial plane, the middle column 606 corresponds to the coronal plane and the right column 607 corresponds to the sagittal plane. The top row represents slices from the volume and the bottom row represents binary coronary vessel masks corresponding to those slices.

The training procedure for networks corresponding to the planes is identical, though each one uses a different set of data. The training is performed using a pair of corresponding CT-segmentation mask images for individual slices or alternatively subvolumes. A set of those forms a single batch. A part of the training set is held out as a validation set.

The segmentation CNN in accordance with one embodiment has a structure as discussed with reference to FIG. 4, with the following differences. The input data is a masked raw volume and (optionally) the corresponding masked volumetric Jerman filter response. If both types of inputs are used, then the segmentation CNNs should include an additional channel for processing that data. The output is a binary coronary vessels mask image containing the location of the coronary vessels.

Next, the coronary vessels masks output for the masked slices for the different planes can be combined to a segmented 3D data set representing the shape, location and size of the coronary vessels.

The training procedure in accordance with one embodiment is equivalent to that discussed in FIG. 5. Upon the completion of the training, the weights of the neural network are stored and can be used for prediction. The input data for the prediction process are CT scan slices of the heart volume with contrast filled coronary arteries masked with the heart region as predicted in the previous steps. For prediction of the location of the coronary vessels slices in the form of the binary mask, the data is propagated through all the layers of the networks, successively, until it reaches the final layer. The output of the final layer is a binary image containing the location of the coronary vessels. The individual prediction of each neural network is an image. As the network makes predictions in a slice by slice, we can reconstruct the volumetric information simply by accumulating the response from all the networks slice by slice in all directions. The volumetric predictions are then combined by averaging the individual results and applying a threshold or other means (majority voting, reconstruction neural network, e.g. 3D Unct).

The predicted binary volume representing the coronary vessels can be subjected to additional post-processing:

During post-processing, connected components comprising fewer than a parameterisable voxel count (for example 100-500 voxels, 200 voxels in one embodiment) may be removed to suppress small false-positive detections.

The cleansed binary mask can then be converted to a watertight triangular surface mesh using a marching-cubes or marching-tetrahedra algorithm. The resulting mesh may be exported in a standard interchange format such as STL, OBJ, or PLY for downstream visualisation, additive manufacturing, or computational fluid-dynamics analysis.

Alternatively, in accordance with a second embodiment of the segmentation procedure, in step 103 B the 3D volume is converted to a first set of subvolumes. Next, in step 104 B, a region of interest is extracted by autonomous segmentation of the heart region as outlined by the pericardium, by means of a neural network trained on 3D subvolumes and combining the results of the individual subvolume predictions for the first set to output a mask denoting a heart region as delineated by the pericardium. In step 106 B, the masked volume is divided into a second set of subvolumes. In step 107 B, the coronary vessel segmentation is performed by a segmentation CNN in a similar way as for pericardium, for the 3D subvolumes of the second obtained in the previous step 106 B. A single segmentation neural network can be used. The output of the final layer of the segmentation network is a binary volume containing the location of the coronary vessels. As the segmentation network makes predictions in a subvolume by subvolume manner, we can reconstruct the volumetric information simply by combining the subvolume predictions. Therefore, the coronary vessels mask output for the different subvolumes can be combined to a segmented 3D data set representing the shape, location and size of the coronary vessels. The other steps of the procedure are equivalent to that described above with respect to the first embodiment of the segmentation procedure.

Adjacent inference sub-volumes can overlap by 25%-50% of their linear dimension to minimise seam artefacts at tile boundaries; predictions in the overlap zone are merged by averaging or by selecting the voxel-wise maximum probability.

The CNNs as shown in FIG. 4, for use in the first embodiment of the segmentation procedure, involving 2D image slices, can be configured as follows:

The CNN as shown in FIG. 4, for use in the second embodiment of the segmentation procedure, involving 3D subvolumes, can be configured as follows:

Examples of desired final results 301, 302, 303 for both embodiments of the segmentation procedure are given in FIG. 3.

The functionality described herein in accordance with one embodiment can be implemented in a computer system 700, such as shown in FIG. 7. The system 700 may include at least one nontransitory processor-readable storage medium 710 that stores at least one of processor-executable instructions 715 or data; and at least one processor 720 communicably coupled to the at least one nontransitory processor-readable storage medium 710. The at least one processor 720 may be configured to (by executing the instructions 715) perform the procedure of FIG. 1.

Inference workloads may be scheduled on one or more graphics-processing units (GPUs), tensor-processing units (TPUs), or other neural-network accelerators when such hardware is present, with automatic fallback to central-processing-unit (CPU) execution.