Fix: Fixed sampling bias in cell_boundaries by excluding endpoint

[mrjholt]

Summary

This PR fixes a logic error in src/cajal/sample_seg.py where the start and end points of closed cell boundaries were being sampled twice, creating duplicate points

Issue

skimage.measure.find_contours returns closed loops where the first and last points are identical. The previous logic used np.linspace(..., endpoint=True), which sampled this geometric vertex twice

Impact

1. Sampling Bias: as it creates a non-uniform distribution of points with an artificial cluster at the "seam" of the polygon, skewing GW distances
2. Runtime Crash: Since both endpoints are included it results in a pairwise distance of 0.0 in the output distance matrices which breaks cajal.utilities.avg_shape because the step_size function returns 0 causing a ZeroDivisionError when the code attempts to normalize the matrix (medoid_matrix / step_size)

The Fix

Updated the np.linspace call to use endpoint=False treating the boundary as a periodic cycle

Verification

I verified this locally with the dataset that was previously crashing avg_shape_spt. The fix resolves the crash and prevents the division-by-zero error.

Closes #20

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