docs: add todays status update presentation

Author: 404Simon

docs/update_06_09.pdf

@@ -0,0 +1,306 @@
+#set page(
+  width: 28.8cm,
+  height: 16.2cm,
+  margin: (top: 1.4cm, bottom: 1cm, left: 1.8cm, right: 1.8cm),
+  fill: white,
+  numbering: "1",
+  number-align: right,
+)
+
+#set text(font: "New Computer Modern", size: 16pt)
+#set par(justify: true, leading: 0.65em)
+
+#let accent = rgb("#1d4ed8")
+#let accent-light = rgb("#eff6ff")
+#let accent-mid = rgb("#93c5fd")
+#let muted = rgb("#6b7280")
+#let dark = rgb("#111827")
+#let green-bg = rgb("#f0fdf4")
+#let green-border = rgb("#bbf7d0")
+#let green-text = rgb("#166534")
+
+#let slide(title, body) = {
+  pagebreak()
+  block(
+    width: 100%,
+    inset: (top: 8pt, bottom: 10pt, left: 0pt, right: 0pt),
+    stroke: (bottom: 2.5pt + accent),
+  )[
+    #text(size: 24pt, fill: accent, weight: "bold")[#title]
+  ]
+  v(0.5cm)
+  body
+}
+
+#let highlight(body) = {
+  block(
+    width: 100%,
+    fill: accent-light,
+    inset: 14pt,
+    radius: 6pt,
+    stroke: 1pt + accent-mid,
+  )[
+    #body
+  ]
+}
+
+#let proofbox(body) = {
+  block(
+    width: 100%,
+    fill: green-bg,
+    inset: 14pt,
+    radius: 6pt,
+    stroke: 1pt + green-border,
+  )[
+    #body
+  ]
+}
+
+#let color-cell(c, label) = {
+  block(
+    width: 1.2em,
+    height: 1.2em,
+    fill: c,
+    inset: 0pt,
+    align(center + horizon, text(size: 0.55em, weight: "bold", fill: if label == "D" { black } else { white }, label)),
+  )
+}
+
+#let color-row(colors, labels, sz: 1.2em) = {
+  let cells = ()
+  for i in range(colors.len()) {
+    cells.push(block(width: sz, height: sz, fill: colors.at(i), inset: 0pt))
+  }
+  grid(columns: (sz,) * colors.len(), rows: (sz,), gutter: 0pt, ..cells)
+}
+
+#align(center + horizon)[
+  #block(width: 80%)[
+    #align(center)[
+      #text(size: 34pt, fill: accent, weight: "bold")[
+        VesSkel\
+      ]
+      #v(1.0cm)
+      #text(size: 20pt, fill: dark)[
+        Vessel Skeletonization and Graph-Based\
+        Phenotype Analysis in Retinal Fundus Images
+      ]
+      #v(1.2cm)
+      #line(length: 40%, stroke: 1.5pt + accent)
+      #v(0.8cm)
+      #text(size: 16pt, fill: muted)[
+        Simon Wittmann
+
+        Supervisor: Anna Möller
+      ]
+      #v(0.3cm)
+      #text(size: 14pt, fill: muted)[
+        09. Juni 2026
+      ]
+    ]
+  ]
+]
+
+#slide("Implementation Progress")[
+  #v(-0.3cm)
+  + Euclidean Distance Transformation #sym.arrow Radius, Diameter
+  + 39 features implemented (87 in comparison table)
+  + 2D Simple Point LUT
+  + Warning for unknown config settings
+  + lots of new tests (9 files, #sym.arrow 150+ tests)
+  + (not) running regression tests
+  + parallelized batching (`-j` / `--jobs N`)
+  + fast! shell completions (800 ms #sym.arrow 81 ms)
+  + wavefront experiment
+  + graph cleanup
+]
+
+#slide("Vessel Radius & Diameter via EDT")[
+  The *Euclidean Distance Transform* (EDT) computes the distance from each foreground pixel to the nearest background pixel.
+  Sampling the EDT at skeleton positions yields the *local vessel radius* at every centerline point.
+
+  #v(0.5cm)
+  #table(
+    columns: (1fr, 1fr),
+    stroke: 0.5pt + rgb("#e5e7eb"),
+    inset: 8pt,
+    fill: (x, y) => if y == 0 { accent-light } else if calc.odd(y) { rgb("#f9fafb") },
+    [*Global Statistics*], [*Per-Segment Statistics*],
+    [mean, std, min, max radius], [mean, std, min, max radius],
+    [mean, std, min, max diameter], [mean, std, min, max diameter],
+  )
+
+  #v(0.4cm)
+  - Napari radius layer
+  - used for junction cleanup diameter estimation
+  - toggleable via `"vessel_radius": true` in config
+]
+
+#slide("New Features")[
+
+  *Key additions since last update:*
+  + Per-segment radius & diameter stats (mean, std, min, max)
+    - highest feature importance in random forest
+    - plain SVM now achieves highest score
+  + Per-segment tortuosity & straightness
+  + Vessel area & vessel area fraction
+  + HGU (hyphal growth unit = total length / endpoint count)
+]
+
+#slide("2D Simple Point LUT")[
+  The simple point check used a flood-fill DFS for each candidate pixel, walking the 8-neighborhood to count connected foreground components. This is called *millions of times* during thinning.
+
+  *Solution:* Precompute the simple point status for all 256 possible 8-neighborhood patterns.
+
+  #v(0.3cm)
+  #highlight[
+    The LUT is built at module level and shared across all thinning calls via `@njit(cache=True)`.
+  ]
+
+  #v(0.3cm)
+  The same approach cannot be used for 3D (26-neighborhood = $2^26$ entries = 67 million -- too large). The 3D implementation keeps the octant-based Euler check.
+]
+
+#slide("Junction Triangle Cleanup I")[
+  *Problem:* Wide vessel junctions appear as thick blobs to the thinning algorithm. Instead of producing a single junction pixel, the skeleton contains small triangle- or diamond-shaped cycles:
+
+  #align(center)[
+    ```text
+         o                        ---o---
+        / \                      /   |   \
+       /   \        ->          /    |    \
+      o-----o                  /     |     \
+     /   |   \
+    ```
+  ]
+
+  These artifacts inflate bifurcation counts and distort topology metrics.
+
+  *Simple Algorithm:*
+  + Build junction graph via skan #sym.arrow find cycles via networkx
+  + Compute perimeter of each cycle (sum of Euclidean edge lengths)
+  + Estimate local vessel diameter from EDT at cycle node positions
+  + Filter: collapse cycles where $"perimeter" < "threshold_factor" times "diameter"$
+  + Merge overlapping cycles #sym.arrow collapse to centroid
+  + Reconnect external vessel arms
+
+]
+
+#slide("Junction Triangle Cleanup II")[
+
+  #v(0.3cm)
+  *New Config Values:*
+  - `"junction_cleanup": true`
+  - `"cleanup_threshold_factor": 5.0` (range 2.5–10)
+
+  #highlight[
+    When cleanup is enabled, the cleaned skeleton replaces the original for *every* downstream stage: saved files, summary features, napari layers.
+  ]
+]
+
+#slide("CLI Improvements")[
+  #columns(2, gutter: 1.5em)[
+
+    *Parallel Batch Processing*
+    + `vesskel run --jobs N` / `-j N`
+    + `ProcessPoolExecutor` spawns workers
+    + Each worker processes one image independently
+    + Errors are collected, processing continues
+    + full HRF now takes #sym.approx 23s instead of before \ #sym.approx 50s on my machine
+
+    *Config quality-of-life*
+    + Unknown keys in config JSON produce a warning
+    #colbreak()
+
+    *Shell Completions*
+    + Generated via `argcomplete`
+    + naive: #sym.approx 800 ms (numpy, PIL, pipeline imported eagerly)
+    + After: #sym.approx 81 ms (heavy imports deferred after argcomplete guard)
+    + Supports bash, zsh, powershell
+    + Regression test enforces completion speed
+  ]
+]
+
+#slide("Testing")[
+  #table(
+    columns: (auto, auto, 1fr),
+    stroke: 0.5pt + rgb("#e5e7eb"),
+    inset: 7pt,
+    fill: (x, y) => if y == 0 { accent-light } else if calc.odd(y) { rgb("#f9fafb") },
+    [*File*], [*Tests*], [*Scope*],
+    [`test_cli.py`], [~25], [completions, input discovery, batch (seq + par)],
+    [`test_config.py`], [~20], [serialization, validation, unknown keys],
+    [`test_features.py`], [~20], [tortuosity, fractal dim, radii, graph],
+    [`test_pipeline.py`], [~25], [full pipeline, all option toggles],
+    [`test_napari_layers.py`], [~18], [layer generation, toggle combos],
+    [`test_utils.py`], [~12], [`to_binary` behavior],
+    [`test_2d_thinning_regression.py`], [45], [HRF images vs saved baselines],
+    [`test_3d_thinning_regression.py`], [1], [brain volume vs saved baselines],
+    [`test_3d_skimage_comparison.py`], [1], [vesskel = skimage bit-identical],
+  )
+
+  + Regression tests now marked `@pytest.mark.slow`
+  + Removed parallel test runner, not really needed anymore
+]
+
+#slide("Wavefront Experiment")[
+  #columns(2, gutter: 1.5em)[
+
+    *Problem:* The recheck-removal phase is sequential -- removing pixel A invalidates B's precomputed simplicity check.
+
+    *Key insight:* Two pixels can be processed in parallel if they are *not* 8-neighbors. The 8-neighbor graph on a 2D grid has chromatic number $4$.
+
+    #sym.arrow Color pixels by $(c % 2, r % 2)$:
+    #grid(
+      columns: (1.2em, 1.2em, 1.2em, 1.2em, 1.2em, 1.2em, 1.2em, 1.2em),
+      rows: (1.2em, 1.2em, 1.2em, 1.2em),
+      gutter: 0pt,
+      stroke: 0.3pt + rgb("#cccccc"),
+      align: center + horizon,
+      ..range(32).map(i => {
+        let r = calc.quo(i, 8)
+        let c = calc.rem(i, 8)
+        let cpar = calc.rem(c, 2)
+        let rpar = calc.rem(r, 2)
+        if cpar == 0 and rpar == 0 {
+          color-cell(rgb("#4A90D9"), "A")
+        } else if cpar == 1 and rpar == 0 {
+          color-cell(rgb("#E67E22"), "B")
+        } else if cpar == 0 and rpar == 1 {
+          color-cell(rgb("#27AE60"), "C")
+        } else {
+          color-cell(rgb("#F1C40F"), "D")
+        }
+      })
+    )
+
+    #colbreak()
+
+    *Results:*
+    + neglegible performance improvements (x% - 1x% faster) (second phase is not that time consuming in general)
+    + different output, missing branches, not medial axis thinning anymore
+  ]
+]
+
+#slide("Summary & Next Steps")[
+
+  #columns(2, gutter: 1.5em)[
+    *What I did:*
+    + EDT-based vessel radius & diameter (global, per-segment)
+    + more new features (now 39)
+    + 2D Simple Point LUT #sym.arrow faster thinning
+    + junction cleanup
+    + cli improvements (parallel batching, completions, config warnings)
+    + more tests
+    + Wavefront experiment (4-coloring insight)
+
+    #colbreak()
+
+    *Next steps:*
+    + Group Feature Table into Categories
+    + more features
+    + 3D experiments with graph cleanup
+    + more preprocessing options? simple hole filling could help on some cases in HRF
+    + revisit phenotype classification
+  ]
+]