telemed

Direct-read pipeline for Telemed ultrasound .tvd recordings — HDF5 metadata + per-panel mp4 with inner-image autocrop.

Extracts .tvd recordings to HDF5 sidecars via the AutoInt1 COM API, then encodes to mp4 for downstream DLC / DUSTrack consumption. This document is a reference for the design decisions baked into the package; the per-function docstrings cover the surface in detail.

Install

pip install telemed

The Windows-only pywin32 dependency (required by the .tvd → .tvd.h5 extract path, which talks to EchoWave via COM) installs automatically on Windows and is skipped on macOS / Linux. Non-Windows installs still get the encode + analyze paths (export_video, process() over Set B .tvd.h5 inputs, Log), they just can’t run COM extraction.

ffmpeg must be on PATH (used for the h265 encode).

Extract-path prerequisites (export_h5 / process)

Reading .tvd files goes through EchoWave’s AutoInt1 COM server, so beyond pip install telemed the extract path additionally needs, on the Windows machine that has the device software:

1. Echo Wave II installed (the Telemed vendor application). The COM server ships inside it — there’s nothing to download separately.

2. A one-time COM registration of the AutoInt1 ProgID — run AutoInt1_regasm.bat from an Administrator PowerShell (full command below).

3. Admin EchoWave II + Admin Python per session (COM ROT publication is per-elevation — a non-elevated client can’t see an elevated server).

Full step-by-step in Per-session setup below. The encode + analyze paths (export_video, Log, process() over already-extracted .tvd.h5 files) need none of this — just the pip install + ffmpeg.

Quickstart

import telemed

# Start EchoWave II as Administrator first; start your Python
# session as Administrator too. (COM ROT is per-elevation.)

telemed.process(r"M:/data/pia02")
# Equivalent to:
# telemed.export_h5(r"M:/data/pia02")     # tvd  -> tvd.h5  (Admin + EchoWave)
# telemed.export_video(r"M:/data/pia02")  # tvd.h5 -> mp4(s) (offline)

# Inspect a single recording:
lf = telemed.Log("M:/data/pia02/scan.tvd.h5")
lf.view()                  # matplotlib browser, with scale bar
lf.to_video()              # single-recording mp4 encode

Pipeline

       .tvd  --[COM via AutoInt1]-->  .tvd.h5  --[ffmpeg]-->  .mp4(s)
                export_h5                     export_video
                Admin EchoWave required       offline (no EchoWave)

process() chains both stages on the same source. All three accept a file, folder, or iterable; recursive=True by default; idempotent under skip_existing=True (default).

HDF5 schema (v1)

Composite suffix <stem>.tvd.h5 so downstream glob walks (*.tvd.h5) catch them without picking up unrelated HDF5 data.

Root attributes (flat – no nested groups):

• n_frames, full_frame_width, full_frame_height

• n_b_images – count of active B-mode panels (1 = single probe; 2 = dual probe; up to 4)

• source_tvd_path, extracted_at_iso, schema_version="v1"

• image_dx_cm_per_px, image_dy_cm_per_px – display scale derived from b_depth_mm / 10 / panel_height_px (Telemed support’s “trust the depth setting” calibration). Skipped if b_depth wasn’t captured.

• Per active img_id N ∈ {1, 2, 3, 4} (1=B, 2=B2, 3=B3, 4=B4):

  • roi{N}_x1, roi{N}_x2, roi{N}_y1, roi{N}_y2 (1-based pixel coords matching AutoInt1’s convention)

  • roi{N}_width, roi{N}_height (inclusive pixel counts)

  • physical_dx{N}_cm_per_px, physical_dy{N}_cm_per_px (beamformer-native scale – see “scale” note below)

• param_* – opportunistic ParamGet sweep (~36 fields per recording on real EchoWave acquisitions): probe / beamformer identity, cine end timestamp, B-mode acquisition (depth, frequency, gain, dynamic range, focus, THI, frame averaging, …), geometry / orientation (scan-direction-changed, rotate, view-area, scan-type, …), sanity probes (file-opened, scanning-state, probe-active).

Inner-image autocrop bounds are NOT in the schema. The encoder detects the inner ultrasound image (depth ruler / margins / tick row stripped) from frame pixels at encode time – see “Inner-image autocrop” below. Keeping the bounds out of the sidecar means a detector tweak only requires a re-encode (offline), not a re-extract (Admin EchoWave).

Datasets:

• /timing/frame_idx_1n – int32 (N,)

• /timing/time_ms – float64 (N,), frame 0 anchored at 0.0 ms

• /timing/ifi_ms – float64 (N,), inter-frame intervals

• /frames/gray – uint8 (N, H, W), full-frame display capture (omit by passing frames=False for a fast timing-only extract)

Schema history

version	date	change
v1	2026-05-24	initial public release. Consolidates the
in-development v1a{1..5} series (single ROI / params sweep /
multi-ROI / display-scale capture) into one labelled baseline.

Log reads both the public v1 label and the legacy in-development labels (v1a{1..5}) transparently, so on-disk sidecars produced by pre-graduation pipelines keep loading. Production extracts always write v1. The inner-image autocrop is computed at encode time and doesn’t bump the schema – existing on-disk sidecars get autocropped mp4s “for free” the next time export_video runs over them.

Inner-image autocrop

The panel ROI from AutoInt1’s GetUltrasoundX{1,2}/Y{1,2} is the full B-mode panel: depth ruler + side margins + inner ultrasound image + bottom-tick row. The depth ruler alone eats ~25% of the panel width on single-probe acquisitions, and the bottom-tick row pollutes the bottom with sharp non-anatomical contrast that confuses DLC. We don’t want either in the per-panel mp4.

export_video runs a content-based detector against an aggregate of 16 evenly-spaced frames per panel and crops each mp4 to the inner ultrasound image. Detection failures fall back to the outer panel ROI with a warning. crop="panel" opts out explicitly.

Why at encode time, not extract time? Re-extraction requires Admin EchoWave + Admin Python. The detector is offline (just needs /frames/gray), and encoding is the slow stage anyway – ~50 ms of detection vs. minutes of h265 lossless. Putting detection here means detector tweaks only need a re-encode, not a re-extract.

Algorithm

• Cols. Estimate the EchoWave UI gray level from the panel’s leftmost+rightmost five cols (taking the median to ignore the depth-ruler “0” digit’s bright pixels). A column is “margin” when its mean is within ~12 of that gray AND its vertical std is low (~12); the longest contiguous run of non-margin cols is the inner image width. Tick rows are pre-trimmed for this pass so a saturated bright stripe doesn’t poison the per-col std.

• Rows. Walk up from the last panel row, peeling off rows whose col-restricted mean exceeds the tick threshold (max(60, 2*median + 20)). Top edge of the inner image is the panel top (probed Telemed configurations place the depth-ruler “0” digit above the panel ROI’s y1).

Why content-based detection (not a probe-aperture lookup)

The probe-table approach (predict inner width from probe_name + aperture_mm + image_d{x,y}_cm_per_px) was sketched + dismissed 2026-05-24: predicted width was ~4-5% off observed (670 vs 700 px on the LF9-5N60-A3 probe), meaning a per-probe empirical correction table would be needed anyway. The content-based detector self-calibrates from the actual pixels and generalises to unfamiliar probes without a maintenance table.

For a fixed (probe, depth, view_area, panel_dims), the detected inner ROI is deterministic across recordings of the same acquisition config – a useful invariant for cross-file consistency audits.

Failure mode: fully-black / degenerate recordings

_detect_image_roi returns None when the gray-margin step isn’t clear (no detectable inner-image bounds, or the resulting box is < 20% of the panel area on either axis). The encoder falls back to the panel ROI with a warning so the regression is visible.

Multi-probe auto-detection

A dual-probe acquisition lights up two B-mode panels side-by-side (B + B2 in AutoInt1’s enum, img_id=1 + img_id=2). The authoritative detection signal is the ROI enumeration itself: _collect_b_mode_rois probes img_ids 1-4 and keeps every panel that returns a positive-area rectangle. The number of populated ROIs = number of physical probes in use.

Why not scanning_state (id 200)? It’s a useful sanity-check but has undocumented sub-states. The 2026-05-24 pia02 probe reported state=25, which isn’t in any documented id_state_bb_* constant. The ROI count never ambiguates.

export_video follows: single-probe -> <stem>.mp4; dual-probe -> <stem>_b1.mp4 + <stem>_b2.mp4.

Inactive-panel sentinel: (0, 0, 0, 0)

AutoInt1 returns the zero-rect sentinel (x1, x2, y1, y2) = (0, 0, 0, 0) for inactive img_ids rather than raising. The 2026-05-24 metadata probes on usl02 (single-probe) revealed B2/B3/B4 all coming back as zero-rect. The TelemedRoi.from_cmd validator rejects anything that isn’t a strict positive-area rectangle (x2 > x1 AND y2 > y1), catching both the sentinel and any inverted/negative rect. Without that fix, single-probe recordings would have been mis-classified as quad-probe and produced four _b{N}.mp4 files (three of them degenerate).

Encode pipeline: lossless h265 mono, ultrafast preset

Why lossless

The source /frames/gray is uint8 grayscale straight off the beamformer – no upstream lossy step to reclaim quality from. A CRF-tuned encode is buying file size at the cost of DLC accuracy that the device gave us for free. The cropped per-panel ROI is small enough (~700x550) that lossless h265 lands at ~3 GB per 20k-frame recording, tolerable at corpus scale.

export_video(..., lossless=False, crf=N) is available as an opt-in for users who’d rather trade a few percent of accuracy for ~50x smaller files.

Why preset="ultrafast"

For lossless h265 the preset only trades file size against encode

• decode speed – reconstructed pixels are bit-exact regardless. ultrafast wins on every time axis (encode + linear decode + random seek + TOC build) at +6% size over fast, ~+15% over slow. Full bench table + methodology + raw numbers in BENCHMARKING.md. DLC inference is GPU-bound (~175 fps ceiling), so encoder preset is effectively neutral for inference; the decode + seek wins are what matter for interactive use (DUSTrack labeling, frame scrubbing).

Accuracy invariance: all four lossless presets produce DLC predictions identical within float32 noise (max |delta| < 1e-4 px). The lossy legacy h264/yuv420p path costs ~0.58 px median DLC keypoint error and outliers up to 41 px – the lossless h265 mono pipeline closes that gap. Parity bench data in BENCHMARKING.md.

Dustrack UI fps: lossless h265 mono is ~22% faster in the dustrack UI than legacy h264 yuv420p (42 fps vs 34 fps on the encoding-axis adapter probe). Within the lossless presets the choice is noise (~5% spread). Stacks with the existing fast_render Tier 2 architectural 3.94x gain from dustrack/BENCHMARKING.md.

Power-user overrides:

• preset="slow" for the smallest lossless files (at ~7x slower encode + ~2x slower decode; accuracy-equivalent).

• lossless=False, crf=22 for the smallest files at the ~0.6 px accuracy penalty (~50x smaller than lossless).

CPU vs GPU

We stick with CPU encoding (libx265). GPU options were investigated 2026-05-24 and rejected:

• NVENC h264 has -preset lossless / losslesshp, but h264 lossless of monochrome content runs 2-3x larger than h265 lossless, and NVENC typically converts gray to yuv420p with synthetic chroma planes (defeats the chroma-free pipeline).

• NVENC h265 lossless support is inconsistent across drivers and often not bit-exact.

• Other GPU paths (AMD VCN, Intel QSV) have similar limitations.

h264_nvenc -preset slow -rc constqp -qp 0 is fast and visually indistinguishable from lossless for spot-checks, but it’s not bit-exact and uses yuv420p – not a production output.

TOC creation (datanavigator + PyAV)

datanavigator.VideoReader (the PyAV+TOC backend used by DUSTrack / datanavigator) builds a packet-level index the first time it opens an mp4 and caches it as <mp4>.dnav-toc next to the video. TOC build time follows the same preset curve as decode speed – ultrafast ~2x faster than slow. See BENCHMARKING.md for raw numbers.

Orientation normalisation

EchoWave operators can toggle scan-direction (L/R flip) and rotation per machine. If different machines in a cohort save with different orientations, the same anatomy appears mirrored/rotated across recordings – catastrophic for cohort-wide DLC training.

The schema captures the L/R-flip state via b_is_scan_direction_changed (AutoInt1 id 133, bool) and rotation via b_rotate (id 132, int). export_video applies -vf hflip when the flip flag is True, so every cohort mp4 lands in a canonical orientation regardless of which operator toggled what on which machine. normalize_orientation=False disables this for power-user inspection.

Known limitation: U/D flip

AutoInt1’s id_b_flip_up_down (105) is action-only – there’s no companion getter. So U/D flip cannot be detected from the sidecar.

Mitigation: lock down the acquisition SOP (“never toggle U/D flip”) and visually spot-check representative frames per machine during cohort onboarding. If a U/D mismatch is found, the operator needs to either re-acquire or manually flag the affected recordings.

Known limitation: rotation enum

b_rotate returns an int (0/1/2/3? or actual degrees?) – the AutoInt1 docs don’t specify the mapping. Both probed cohorts (usl02, pia02) report 0. When a non-zero b_rotate is encountered, _orientation_vf warns + leaves the pixels untouched; the user should investigate and update the function once the mapping is verified against a deliberately-rotated recording.

Spatial scale: physical (beamformer) vs image (display)

There are two scales to be aware of, and they differ by ~2% on typical Telemed acquisitions:

Attribute	Returns	Use for…
Log.physical_dx_cm_per_px	beamformer-native sample spacing (from GetUltrasoundPhysicalDeltaX)	hardware provenance; not measurement
Log.image_dx_cm_per_px	display scale – b_depth_mm / 10 / panel_height_px	cm conversions on tracked-point coords

The reported physical_dx is the beamformer’s native radial sample spacing – a function of the ADC clock and the assumed speed of sound. But EchoWave renders the resulting image onto a display frame whose height is laid out to match the operator-selected depth setting (the depth ruler IS the trusted calibration). For a 50 mm depth setting on a 558 px panel:

• physical_dy_cm_per_px = 0.009166 cm/px (=> 558 × dy = 5.11 cm)

• image_dy_cm_per_px = 0.05 cm / 558 px = 0.00896 cm/px (=> 5.00 cm)

Per Telemed support: trust the depth setting. So image_d{x,y} is what you want for spatial measurements on DLC keypoints. Both attributes return None for v1 sidecars that lack params["b_depth"].

The display x scale is assumed equal to the y scale (image_dx == image_dy) because Telemed renders with square display pixels (1:1 aspect so anatomy doesn’t squish) and AutoInt1 reports physical_dx == physical_dy on every probed acquisition. If a future probe breaks this assumption it’ll surface as anatomy rendered with a non-1:1 aspect in Log.view(); revisit then.

Timing

/timing/time_ms carries true per-frame timestamps at the device’s native ~100 ns precision (Telemed acquisitions are VFR – the inter- frame interval varies frame to frame around the mean fps; 50% of frames land more than 1 ms off the mean). DLC and cv2 index by frame number, so the encoded mp4 declares CFR at mean_fps – but the .tvd.h5 is the source of truth for real time. Downstream analysis converting tracked points back into the OT clock should round-trip via Log.time_ms[frame_idx], not the mp4’s frame rate.

There is no timing CSV sidecar; an earlier design considered one, but it duplicates data already in the .tvd.h5 (and would risk desyncing).

Last-frame outlier

Both DICOM FrameTimeVector and the COM-extracted IFI show a ~0.078 ms inter-frame interval as the final entry on every recording – recording-termination artifact (compound sub-frame). Harmless if downstream sync work either drops the last frame or weights by IFI.

Per-session setup (Administrator EchoWave + Administrator Python)

The h5 stage (export_h5 / process) requires:

0. One-time per machine: install Echo Wave II (the Telemed vendor application). The AutoInt1 COM server ships inside it; the Config\Plugins folder referenced below is created by the installer. There is nothing to download from this package for it.

1. One-time per machine: register the COM ProgID. From an Administrator PowerShell:

   cd "C:\Program Files\Telemed\Echo Wave II Application\EchoWave II\Config\Plugins"
   .\AutoInt1_regasm.bat
   

   You should see “Types registered successfully”. Without this, GetActiveObject('EchoWave2.CmdInt1') raises “Invalid class string”. (The exact install path can vary by EchoWave version / install location — if Config\Plugins isn’t under that path, search the EchoWave install dir for AutoInt1_regasm.bat.)

2. Per session: start EchoWave II as Administrator (right-click -> “Run as administrator”).

3. Per session: start your Python (or terminal) as Administrator. COM ROT publication is per-elevation; a non-elevated client can’t see an elevated server.

The video stage (export_video) is offline and runs in any Python.

Network drives

EchoWave’s OpenFile fails on UNC / mapped network paths in our setup. export_h5 auto-detects non-C: drives + UNC prefixes, copies each .tvd to a local temp dir, processes there, and copies the resulting .h5 back to the source folder. A ThreadPoolExecutor overlaps the network copies with the COM extraction (stage N+1 + unstage N-1 run on workers while the COM thread handles N).

Implementation notes

Pipe deadlock (libx265 + Popen)

libx265 prints per-frame statistics to stderr by default. Reading frames from stdin via subprocess.Popen(..., stderr=PIPE) deadlocks once the stderr buffer (~64 KB) fills – the encoder blocks waiting for the reader, the Python frame loop blocks waiting for the encoder. The cmd builder mandates -hide_banner -loglevel error so stderr stays small enough to never fill.

pywin32 + .NET CCW: zero-arg methods are properties

AutoInt1’s zero-argument calls (GetFramesCount, GetCurrentFrameTime, etc.) expose as properties under pywin32’s late binding, not callables. Drop the parens:

n = cmd.GetFramesCount     # works
n = cmd.GetFramesCount()   # TypeError

Methods with arguments use normal parens. This bit the initial probe script; it’s wrapped inside the module now.

Related files

• BENCHMARKING.md – preset bench tables (encode / decode / seek), DLC accuracy parity data (lossless h265 mono vs legacy h264 yuv420p), and TOC build numbers.

• _metadata_probe.py – parses AutoInt1Client.txt to classify all ~378 documented ids by extraction strategy; the source-of-truth for the _PARAM_SPECS curation.

Acknowledgments

This package was developed as part of the ImmersionToolbox initiative at the MIT.nano Immersion Lab. Thanks to NCSOFT for supporting this initiative.