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import matplotlib.pyplot as plt
import numpy as np
import SimpleITK as sitk
from armscan_env.clustering import (
    TissueClusters,
    TissueLabel,
    find_DBSCAN_clusters,
)
from armscan_env.config import get_config
from armscan_env.envs.rewards import anatomy_based_rwd
from armscan_env.util.visualizations import show_clusters

config = get_config()

DBSCAN Clustering Search

volume = sitk.ReadImage(config.get_single_labelmap_path(1))
volume_img = sitk.GetArrayFromImage(volume)
print(f"{volume_img.shape=}")

size = np.array(volume.GetSize()) * np.array(volume.GetSpacing())
print(f"{size=} mm")
transversal_extent = (0, size[0], 0, size[2])
longitudinal_extent = (0, size[1], 0, size[2])
frontal_extent = (0, size[0], size[1], 0)

volume_img.shape=(61, 864, 606)
size=array([150.0486095 , 213.93069077,  61.        ]) mm

tissues = {
    "bones": 1,
    "tendons": 2,
    "ulnar": 3,
}

DBSCAN might be a better clustering technique that offers more flexibility. It works by defining clusters as continuous regions of high density. It groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). The DBSCAN algorithm has two parameters: min_samples and eps. The min_samples parameter specifies the minimum number of points that a cluster must have in order to be considered a cluster. The eps parameter specifies the maximum distance between two samples for one to be considered as in the neighborhood of the other.

clusters_680 = TissueClusters(
    bones=find_DBSCAN_clusters(
        TissueLabel.BONES,
        volume_img[:, 680, :].T,
        eps=4.1,
        min_samples=46,
    ),
    tendons=find_DBSCAN_clusters(
        TissueLabel.TENDONS,
        volume_img[:, 680, :].T,
        eps=3.0,
        min_samples=18,
    ),
    ulnar=find_DBSCAN_clusters(
        TissueLabel.ULNAR,
        volume_img[:, 680, :].T,
        eps=2.0,
        min_samples=10,
    ),
)

fig = show_clusters(clusters_680, volume_img[:, 680, :].T, extent=transversal_extent)
fig.set_xlabel("X [mm]")
fig.set_ylabel("Z [mm]")
plt.show()

In general, this algorithm offers a better anatomical description, since it allows to reason about the average dimension of the clusters for each kind of tissue, removing possible outliers given by segmentation errors.

You can play around with the parameter of DBSCAN to find the best tuning, or you can use the constructor TissueClusters.from_labelmap_slice which iterates through the tissues to find clusters with predetermined parameters eps and min_samples.

sweep_loss = []
clusters_list = []

for i in range(volume_img.shape[1]):
    clusters = TissueClusters.from_labelmap_slice(volume_img[:, i, :].T)
    loss = anatomy_based_rwd(clusters, n_landmarks=(7, 3, 1))
    clusters_list.append(clusters)
    print(f"Loss for slice {i}: {loss}")
    sweep_loss.append(loss)

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Loss for slice 555: -0.1818181818181818
Loss for slice 556: -0.15151515151515152
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Loss for slice 559: -0.15151515151515152
Loss for slice 560: -0.1818181818181818
Loss for slice 561: -0.2121212121212121
Loss for slice 562: -0.2121212121212121
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Loss for slice 578: -0.2121212121212121
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Loss for slice 581: -0.1818181818181818
Loss for slice 582: -0.15151515151515152
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Loss for slice 584: -0.1818181818181818
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Loss for slice 586: -0.1818181818181818
Loss for slice 587: -0.1818181818181818
Loss for slice 588: -0.15151515151515152
Loss for slice 589: -0.1818181818181818
Loss for slice 590: -0.1818181818181818
Loss for slice 591: -0.5454545454545454
Loss for slice 592: -0.5151515151515151
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Loss for slice 594: -0.0909090909090909
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Loss for slice 599: -0.06060606060606061
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Loss for slice 601: -0.12121212121212122
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Loss for slice 608: -0.12121212121212122
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Loss for slice 616: -0.06060606060606061

Loss for slice 617: -0.0909090909090909
Loss for slice 618: -0.12121212121212122
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Loss for slice 627: -0.1818181818181818
Loss for slice 628: -0.0909090909090909
Loss for slice 629: -0.12121212121212122
Loss for slice 630: -0.15151515151515152
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Loss for slice 636: -0.1818181818181818
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Loss for slice 638: -0.1818181818181818
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Loss for slice 640: -0.1818181818181818

Loss for slice 641: -0.1818181818181818
Loss for slice 642: -0.12121212121212122
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Loss for slice 651: -0.5151515151515151
Loss for slice 652: -0.12121212121212122
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Loss for slice 660: -0.0909090909090909
Loss for slice 661: -0.06060606060606061
Loss for slice 662: -0.06060606060606061

Loss for slice 663: -0.06060606060606061
Loss for slice 664: -0.06060606060606061
Loss for slice 665: -0.06060606060606061
Loss for slice 666: -0.030303030303030304
Loss for slice 667: -0.030303030303030304
Loss for slice 668: -0.0
Loss for slice 669: -0.030303030303030304
Loss for slice 670: -0.030303030303030304
Loss for slice 671: -0.030303030303030304
Loss for slice 672: -0.0
Loss for slice 673: -0.0
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Loss for slice 675: -0.0
Loss for slice 676: -0.030303030303030304
Loss for slice 677: -0.0
Loss for slice 678: -0.030303030303030304
Loss for slice 679: -0.0

Loss for slice 680: -0.0
Loss for slice 681: -0.0
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Loss for slice 685: -0.030303030303030304
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Loss for slice 688: -0.06060606060606061
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Loss for slice 692: -0.0909090909090909
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Loss for slice 694: -0.15151515151515152
Loss for slice 695: -0.12121212121212122
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Loss for slice 700: -0.0909090909090909
Loss for slice 701: -0.12121212121212122
Loss for slice 702: -0.12121212121212122
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Loss for slice 711: -0.12121212121212122
Loss for slice 712: -0.5151515151515151
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Loss for slice 714: -0.0909090909090909
Loss for slice 715: -0.12121212121212122
Loss for slice 716: -0.12121212121212122
Loss for slice 717: -0.0909090909090909
Loss for slice 718: -0.0909090909090909
Loss for slice 719: -0.5151515151515151
Loss for slice 720: -0.48484848484848486
Loss for slice 721: -0.12121212121212122
Loss for slice 722: -0.0909090909090909
Loss for slice 723: -0.12121212121212122
Loss for slice 724: -0.12121212121212122

Loss for slice 725: -0.12121212121212122
Loss for slice 726: -0.12121212121212122
Loss for slice 727: -0.12121212121212122
Loss for slice 728: -0.15151515151515152
Loss for slice 729: -0.15151515151515152
Loss for slice 730: -0.15151515151515152
Loss for slice 731: -0.12121212121212122
Loss for slice 732: -0.48484848484848486
Loss for slice 733: -0.48484848484848486
Loss for slice 734: -0.48484848484848486
Loss for slice 735: -0.48484848484848486
Loss for slice 736: -0.12121212121212122
Loss for slice 737: -0.15151515151515152
Loss for slice 738: -0.15151515151515152
Loss for slice 739: -0.12121212121212122
Loss for slice 740: -0.12121212121212122
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Loss for slice 742: -0.06060606060606061
Loss for slice 743: -0.12121212121212122
Loss for slice 744: -0.5454545454545454
Loss for slice 745: -0.48484848484848486
Loss for slice 746: -0.1818181818181818

Loss for slice 747: -0.5454545454545454
Loss for slice 748: -0.5454545454545454
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Loss for slice 750: -0.1818181818181818
Loss for slice 751: -0.1818181818181818
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Loss for slice 753: -0.24242424242424243
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Loss for slice 755: -0.2727272727272727
Loss for slice 756: -0.24242424242424243
Loss for slice 757: -0.2727272727272727
Loss for slice 758: -0.24242424242424243
Loss for slice 759: -0.2121212121212121
Loss for slice 760: -0.2121212121212121
Loss for slice 761: -0.2121212121212121
Loss for slice 762: -0.2121212121212121
Loss for slice 763: -0.24242424242424243
Loss for slice 764: -0.2121212121212121
Loss for slice 765: -0.1818181818181818
Loss for slice 766: -0.5757575757575757
Loss for slice 767: -0.2121212121212121
Loss for slice 768: -0.2121212121212121

Loss for slice 769: -0.15151515151515152
Loss for slice 770: -0.1818181818181818
Loss for slice 771: -0.1818181818181818
Loss for slice 772: -0.1818181818181818
Loss for slice 773: -0.15151515151515152
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Loss for slice 777: -0.1818181818181818
Loss for slice 778: -0.15151515151515152
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Loss for slice 784: -0.1818181818181818
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Loss for slice 786: -0.2121212121212121
Loss for slice 787: -0.5757575757575757
Loss for slice 788: -0.1818181818181818

Loss for slice 789: -0.5454545454545454
Loss for slice 790: -0.5454545454545454
Loss for slice 791: -0.5454545454545454
Loss for slice 792: -0.15151515151515152
Loss for slice 793: -0.5757575757575757
Loss for slice 794: -0.15151515151515152
Loss for slice 795: -0.2121212121212121
Loss for slice 796: -0.24242424242424243
Loss for slice 797: -0.24242424242424243
Loss for slice 798: -0.1818181818181818
Loss for slice 799: -0.2121212121212121
Loss for slice 800: -0.2121212121212121
Loss for slice 801: -0.24242424242424243
Loss for slice 802: -0.24242424242424243
Loss for slice 803: -0.30303030303030304
Loss for slice 804: -0.24242424242424243
Loss for slice 805: -0.24242424242424243
Loss for slice 806: -0.2727272727272727
Loss for slice 807: -0.2121212121212121
Loss for slice 808: -0.1818181818181818
Loss for slice 809: -0.24242424242424243
Loss for slice 810: -0.2727272727272727
Loss for slice 811: -0.30303030303030304
Loss for slice 812: -0.2727272727272727
Loss for slice 813: -0.2727272727272727

Loss for slice 814: -0.2727272727272727
Loss for slice 815: -0.30303030303030304
Loss for slice 816: -0.2727272727272727
Loss for slice 817: -0.24242424242424243
Loss for slice 818: -0.2121212121212121
Loss for slice 819: -0.2121212121212121
Loss for slice 820: -0.2121212121212121
Loss for slice 821: -0.1818181818181818
Loss for slice 822: -0.15151515151515152
Loss for slice 823: -0.1818181818181818
Loss for slice 824: -0.2121212121212121
Loss for slice 825: -0.1818181818181818
Loss for slice 826: -0.1818181818181818
Loss for slice 827: -0.2121212121212121
Loss for slice 828: -0.2121212121212121
Loss for slice 829: -0.1818181818181818
Loss for slice 830: -0.1818181818181818
Loss for slice 831: -0.2121212121212121
Loss for slice 832: -0.2121212121212121
Loss for slice 833: -0.2121212121212121
Loss for slice 834: -0.2121212121212121
Loss for slice 835: -0.2121212121212121
Loss for slice 836: -0.24242424242424243
Loss for slice 837: -0.24242424242424243
Loss for slice 838: -0.24242424242424243
Loss for slice 839: -0.24242424242424243
Loss for slice 840: -0.24242424242424243
Loss for slice 841: -0.24242424242424243
Loss for slice 842: -0.24242424242424243
Loss for slice 843: -0.24242424242424243
Loss for slice 844: -0.24242424242424243
Loss for slice 845: -0.24242424242424243
Loss for slice 846: -0.24242424242424243
Loss for slice 847: -0.24242424242424243
Loss for slice 848: -0.24242424242424243

Loss for slice 849: -0.24242424242424243
Loss for slice 850: -0.2121212121212121
Loss for slice 851: -0.2121212121212121
Loss for slice 852: -0.2121212121212121
Loss for slice 853: -0.24242424242424243
Loss for slice 854: -0.24242424242424243
Loss for slice 855: -0.1818181818181818
Loss for slice 856: -0.1818181818181818
Loss for slice 857: -0.1818181818181818
Loss for slice 858: -0.1818181818181818
Loss for slice 859: -0.1818181818181818
Loss for slice 860: -0.1818181818181818
Loss for slice 861: -0.6565656565656566
Loss for slice 862: -0.6565656565656566
Loss for slice 863: -0.6565656565656566

Show code cell source Hide code cell source

plt.plot(range(len(sweep_loss)), sweep_loss, marker="o")
plt.xlabel("Slice index")
plt.ylabel("Score")
plt.title("Score along axial slices")

plt.show()

The plot shows the score of the clustering along the axial slices. The optimal region corresponds to the anatomical description, as we can see in the following images.

# Find the max reward and show the slice for it
max_loss_idx = np.argmax(sweep_loss)
print(f"Minimum loss: {sweep_loss[max_loss_idx]} at index {max_loss_idx}")
fig, ax = plt.subplots(1, 2, figsize=(14, 7))
show_clusters(clusters_list[max_loss_idx], volume_img[:, max_loss_idx, :].T, ax=ax[0], aspect=6)
ax[1].imshow(volume_img[:, max_loss_idx, :], aspect=6, origin="lower")
for a in ax:
    a.axis("off")
plt.show()

Minimum loss: -0.0 at index 668

zero_loss_indices = np.where(np.array(sweep_loss) == 0)[0]
print(f"{len(zero_loss_indices)} indices return a zero loss: ", zero_loss_indices)

nrows = 3
ncols = len(zero_loss_indices) // nrows
indices_to_display = nrows * ncols

if indices_to_display > 0:
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(20, 10))
    axes = axes.flatten()
    for i, idx in enumerate(zero_loss_indices[:indices_to_display]):
        axes[i] = show_clusters(
            tissue_clusters=clusters_list[idx],
            slice=volume_img[:, idx, :].T,
            aspect=6,
            ax=axes[i],
        )
        axes[i].set_title(f"Index: {idx}, Loss: {sweep_loss[idx]:.2f}")
        axes[i].axis("off")

    plt.show()

12 indices return a zero loss:  [668 672 673 674 675 677 679 680 681 682 683 684]

Segmenting the same image with the centro-symmetric clustering and DBSCAN demonstrates the difference in the clustering techniques. The centro-symmetric clustering is more sensitive to the noise in the image, while DBSCAN can be tuned to ignore the noise and focus on the main clusters.

from armscan_env.clustering import cluster_iter

fig, ax = plt.subplots(1, 2, figsize=(14, 7))

clusters_668 = cluster_iter(tissues, volume_img[:, 668, :].T)
show_clusters(clusters_668, volume_img[:, 668, :].T, aspect=6, ax=ax[0])
ax[0].axis("off")
ax[0].set_title("Centrosymmetric Clustering")

clusters_668 = TissueClusters.from_labelmap_slice(volume_img[:, 668, :].T)
show_clusters(clusters_668, volume_img[:, 668, :].T, aspect=6, ax=ax[1])
ax[1].axis("off")
ax[1].set_title("DBSCAN Clustering")
plt.show()