Synthetic & Open Data for iUS Tumor Segmentation

MSc Project Data Repository — Last updated 2026-03-23

This repository documents the data, intermediate results, and quality control outputs for three technical routes to train intraoperative ultrasound (iUS) brain tumor segmentation models without manual iUS annotations. Route A transfers MRI tumor labels to iUS space via rigid registration. Route B synthesises realistic iUS from MRI using MMHVAE (Dorent et al., 2025). Route A+B combines both approaches via two-stage training.

All data derives from the ReMIND dataset (Juvekar et al., 2023) — 114 patients, 41 GB DICOM, publicly available from TCIA. Of these, 62 are first-surgery patients suitable for registration; 110 have sufficient anatomy for virtual sweep simulation. The entire pipeline from raw DICOM to training-ready data was executed over 2026-03-16 to 2026-03-18 on a single RTX 2080 Ti machine.

Key Figures at a Glance

114

ReMIND patients (DICOM)

TCIA

73.7%

Registration validity (84/114)

V3 after QC exclusion (30 excluded)

44,502

Route A training slices

43 patients

1,090

Virtual sweeps (Route B)

110 cases × 10

1,878

MMHVAE input volumes

33.7 GB NIfTI

589

QC images (local)

549 mmhvae + 40 sweep

Table 1. Technical Route Comparison

The three routes differ in how training labels are obtained and whether the model sees real or synthetic iUS. Route A is the simplest baseline (direct label transfer with registration noise); Route B eliminates registration error by synthesising iUS from MRI; Route A+B leverages both real and synthetic data.

	Route A (Baseline)	Route B (Core Method)	Route A+B (Innovation)
Pipeline	MRI → Rigid Reg → Pseudo-label → nnU-Net	MRI → Virtual Sweep → MMHVAE → nnU-Net	A pre-train → B fine-tune
Label quality	Noisy (3–5 mm registration error)	Precise (zero registration error)	Complementary
Training images	Real iUS	Synthetic iUS	Both
Data scale	44,502 slices / 43 patients	1,090 sweeps / 110 cases	Combined
DSC reference	0.58–0.62 (Faanes 2025)	0.74 (Dorent 2025)	Target: 0.84
Status	Data ready; training blocked (GPU)	Sweeps done; MMHVAE blocked (weights)	Pending both routes

Detailed Pages: Route A — registration iteration data (V1→V3), convergence analysis, optimizer comparison, pseudo-label summary · Route B — virtual sweep statistics, MMHVAE input QC gallery (549 images), best-effort sweep QC (40 images) · Progress — pipeline status, blockers, DSC roadmap

Table 2. Data File Index

All quantitative results are stored as flat CSV or per-case JSON files. This index lists the primary data files referenced throughout the dashboard; each is linked to the relevant detail page where its contents are analysed.

File	Rows	Key Columns	Route
`registration_results_v1.csv`	204	patient_id, translation_magnitude_mm, mi_improvement, stop_condition, status	A
`registration_results_v2.csv`	114	+ convergence, mask_coverage	A
`registration_results_v3.csv`	114	+ convergence, mask_coverage	A
`ab_test_20260317_075447.csv`	70	config (7 optimizers × 10 patients), converged, stop_class	A
`sweep_metadata.json` × 110	~10 sweeps each	tumor_diameter_mm, C2_dist_to_tumor, tumor_pixels, saved_slices	B
`conversion_*.csv`	~1090	case_id, modalities, volume_shape, seg_nonzero	B