@@ -300,7 +300,6 @@ public static SomaResult detectSomaAt(
300300 * }</pre>
301301 * Otherwise, the tracer's heuristic reduction pipeline may discard valid
302302 * somas. See {@link AbstractGWDTTracer#setAutoFilter(boolean)}.
303- * </p>
304303 *
305304 * @param source input image (2D)
306305 * @param seeds list of {@code long[]{x, y}} pixel coordinates
@@ -412,7 +411,6 @@ public static List<SomaResult> detectSomasAt(
412411 * tracer.setAutoFilter(false);
413412 * List<Tree> trees = tracer.traceMultiSoma(somas);
414413 * }</pre>
415- * </p>
416414 *
417415 * @param imp the source image (ImagePlus)
418416 * @param rois ROIs whose centroids serve as soma seeds
@@ -699,7 +697,6 @@ public static List<SomaResult> detectAllSomas(
699697 * requiring the user to specify one.</li>
700698 * </ul>
701699 * For 2D images, {@code zSlice} is ignored.
702- * </p>
703700 *
704701 * @param source input image (2D or 3D ImgPlus)
705702 * @param threshold intensity threshold. Use -1 for Otsu, NaN for mean.
@@ -1116,15 +1113,13 @@ public static double estimateMinSomaDistance(final List<SomaResult> somas) {
11161113 * clustering on the surviving somas, find the largest ratio gap in
11171114 * merge heights, return the geometric mean of the gap boundaries</li>
11181115 * </ol>
1119- * </p>
11201116 * <p>
11211117 * <b>Experimental:</b> The quality of the estimate depends heavily on
11221118 * step 1 (thickness filtering), which has known limitations for images
11231119 * with large connected foreground regions, see
11241120 * {@link #filterSomasByThickness} javadoc. The gap analysis in step 2 is
11251121 * well-founded (standard hierarchical clustering), but is only as good as
11261122 * the input somas it receives.
1127- * </p>
11281123 *
11291124 * @param <T> pixel type
11301125 * @param somas list of detected somas (at least 3 required)
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