HistoPipe: Modular Pipelines for Histology

• HistoPipe is a modular pipeline paradigm in histology that decomposes workflows into separable stages with explicit intermediate data for expert intervention.
• It integrates expert-driven synthetic data generation, instance segmentation, and diagnostic classification techniques for robust histological analysis.
• Its flexible orchestration supports diverse applications from cell instance segmentation to whole-slide processing and QC in digital pathology.

HistoPipe is a name used in recent literature for modular pipelines in histological image analysis. In one usage, it denotes an expert-driven method for generating synthetic training data for histological cell instance segmentation from only a handful of annotated images (Basla et al., 2024). In other usages, it denotes a broader pipeline pattern for object-centric diagnosis, whole-slide segmentation and pathomics, and quality-control front ends in digital pathology (Li et al., 2020, Keller et al., 22 Oct 2025, Patil et al., 2024, Patil et al., 2024). An unrelated later paper reused the same name for a history-driven rollout scheduler in LLM reinforcement learning (He et al., 26 Aug 2025). The literature therefore suggests that, within histopathology, HistoPipe is best understood less as a single canonical package than as a modular orchestration paradigm whose stages, intermediate representations, and expert intervention points vary with the task.

1. Nomenclature and scope

One source of confusion is that HistoPipe does not denote one universally fixed architecture across the cited literature. The name has been attached to synthetic data generation, diagnostic classification, whole-slide pathomics, QC, and, outside histology, rollout scheduling (Basla et al., 2024, Li et al., 2020, Keller et al., 22 Oct 2025, He et al., 26 Aug 2025).

Usage	Core formulation	Source
Synthetic training data for histological cell instance segmentation	Blob generation, blob placement, AdaIN image synthesis	(Basla et al., 2024)
Generalizable object-centric diagnostic pipeline	Duct instance segmentation, tissue segmentation, multi-level features, classifier	(Li et al., 2020)
Whole-slide kidney processing and visualization	FTU segmentation, feature extraction, OME-TIFF/OME-NGFF, AnnData/MuData/SpatialData, Vitessce	(Keller et al., 22 Oct 2025)
WSI QC and ROI selection	HistoROI patch classification; tissue/blur/fold/pen segmentation	(Patil et al., 2024, Patil et al., 2024)
HistoPipe-equivalent whole-brain registration	Histology/blockface/MRI preprocessing and diffeomorphic registration	(Alegro et al., 2019)
Unrelated homonymous scheduler in LLM RL	Two-tier history-driven rollout scheduling	(He et al., 26 Aug 2025)

Across the histology papers, several recurring properties are explicit. Pipelines are decomposed into separable stages; they expose intermediate objects such as instance masks, tissue masks, feature tables, or QC masks; and they are designed so that expert priors, annotator review, or post hoc inspection can steer the workflow. A plausible implication is that the term functions as a systems-level label for modularity, explicit data contracts, and domain control rather than for a single model family.

2. Expert-driven synthetic data generation for cell instance segmentation

In (Basla et al., 2024), HistoPipe is an expert-driven pipeline for synthetic training data in histological cell instance segmentation. The problem setting is low-data nuclei or cell instance segmentation, where pixel-accurate expert masks are scarce and expensive. The input is a small annotated training set

$T = \{(I_j, M_j)\}_{j=1..J},$

with $J$ small and $K$ total nuclei (“blobs”), and the output is a large synthetic training set

$S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$

with $N \gg J$. The downstream labels are per-object binary masks

$M \in \{0,1\}^{h \times w \times q},$

where $q$ is the number of instances in an image.

The pipeline has three stages. The first stage, blob generation, synthesizes shapes by interpolating pairs of real blob contours. After sampling $E$ equally spaced contour points on two real blobs and aligning them via ICP, a synthetic contour is generated by

$$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$

Morphological area closing then fills the contour to obtain a blob mask $b_\ell$. The method is explicitly characterized as neither parametric ellipses nor learned generative modeling; it is morphology-preserving interpolation between real exemplars. The stated motivation is a homotopy-based view in which 2D masks are projections or slices of 3D nuclei, so continuous contour deformation between masks approximates new views.

The second stage, blob placement, models spatial organization. For each synthetic image, HistoPipe initializes an empty mask $J$0 and an availability map $J$1, defines

$J$2

samples a location $J$3 and a spacing radius $J$4, and greedily places the first blob that fits without overlap. Availability is then updated by

$J$5

Here $J$6 is a density prior, instantiated in the experiments with Perlin noise fitted to blurred real masks, and $J$7 is a spacing distribution reflecting tissue-dependent crowding. No explicit energy minimization is solved, but the paper states that the greedy strategy effectively enforces no-overlap and spacing constraints while adhering to $J$8 more faithfully than random weighted placement.

The third stage, image generation, synthesizes appearance with AdaIN. Given content features $J$9 and style features $K$0, the AdaIN operator is

$K$1

The content is the flattened version of $K$2, and the style is a reference tile $K$3 from a real image chosen to have a similar blob count to $K$4. AdaIN is trained for 30,000 epochs with flips and affine DA on both content and style, plus photometric DA on style only. The resulting $K$5 is paired with $K$6, yielding pixel-perfect labels.

The computational profile is explicit. Blob interpolation has complexity $K$7. Greedy placement has complexity

$K$8

with $K$9 the average blob area, and is identified as the most computationally demanding stage, although highly parallelizable.

Empirically, the pipeline is validated with HoVerNet on fluorescence microscopy images from BBBC for training and NucleusSegData for testing. A full BBBC baseline uses 815 annotated images and approximately 50,000 blobs; HoVerNet trained on this full set achieves DICE $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$0, DICE2 $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$1, AJI $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$2, and AJI$S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$3 $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$4. In the low-data regime, the main result is that starting from 2 real images with $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$5 blobs, training on real data only gives DICE $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$6, DICE2 $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$7, AJI $S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$8, AJI$S = \{(\widetilde I_n, \widetilde M_n)\}_{j=1..N},$9 $N \gg J$0, whereas training on HistoPipe-generated data gives DICE $N \gg J$1, DICE2 $N \gg J$2, AJI $N \gg J$3, AJI$N \gg J$4 $N \gg J$5. With 5 real images ($N \gg J$6), real-only training yields DICE $N \gg J$7, DICE2 $N \gg J$8, AJI $N \gg J$9, AJI$M \in \{0,1\}^{h \times w \times q},$0 $M \in \{0,1\}^{h \times w \times q},$1, and generated training yields DICE $M \in \{0,1\}^{h \times w \times q},$2, DICE2 $M \in \{0,1\}^{h \times w \times q},$3, AJI $M \in \{0,1\}^{h \times w \times q},$4, AJI$M \in \{0,1\}^{h \times w \times q},$5 $M \in \{0,1\}^{h \times w \times q},$6. With 10 real images ($M \in \{0,1\}^{h \times w \times q},$7), real-only training yields DICE $M \in \{0,1\}^{h \times w \times q},$8, DICE2 $M \in \{0,1\}^{h \times w \times q},$9, AJI $q$0, AJI$q$1 $q$2, and generated training yields DICE $q$3, DICE2 $q$4, AJI $q$5, AJI$q$6 $q$7. The paper states that gains largely diminish around 10 images, approximately 1,000 blobs.

Distributional validation of shapes compares area and aspect-ratio statistics. Real blobs have area median $q$8 px with IQR $q$9, versus generated blobs with median $E$0 px and IQR $E$1. Real aspect ratio has median $E$2 with IQR $E$3, versus generated median $E$4 and IQR $E$5. The paper interprets successful training on synthetic data and testing on real histology as evidence that shapes, spatial distributions, and textures are sufficiently realistic for transfer.

The main limitations are also explicit. Style mismatch can induce artifacts or faint nuclei if the style tile has too many or too few blobs relative to $E$6. Rare morphologies may be underrepresented because interpolation assumes homotopic equivalence. The Perlin-noise $E$7 captures coarse density rather than complex tissue architecture such as glandular arrangements or stromal boundaries. Strict exclusion may underfit crowded tissues, and domain shift is not fully addressed.

3. Object-centric diagnosis: DIOP as a HistoPipe instantiation

The breast-histopathology paper (Li et al., 2020) presents the Ductal Instance-Oriented Pipeline as a concrete instantiation of a generalizable HistoPipe. Its premise is that pathologists focus on ducts and lobules because most breast cancers begin in terminal ductal or lobular units. DIOP operationalizes that premise with three modules: duct-level instance segmentation, tissue-level semantic segmentation, and three-level feature aggregation for diagnostic classification.

The instance stage uses Mask R-CNN with a ResNet-50 backbone pretrained on MS COCO, FPN, RPN, and three heads for classification, bounding-box regression, and per-instance binary masks. A ductal instance is defined as all pixels belonging to a single breast duct or lobule cross-section. The pipeline returns, for each detected instance $E$8, a bounding box $E$9, a pixel mask $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$0, and a class score $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$1. Duct masks are “silver-standard” labels derived by combining annotator-provided bounding boxes with tissue-level semantic predictions. The dataset contains 4,347 duct instances across 100 ROIs, with an 80/20 split for instance segmentation, and the reported validation performance is mean IoU $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$2 and mAP $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$3.

The tissue-context stage uses an off-the-shelf multi-resolution encoder-decoder network with eight tissue classes: BG, BE, ME, NS, DS, SC, BL, and NC. Tissue labels are intersected with duct masks and bounding boxes to compute localized tissue distributions and co-occurrence relationships. For a support $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$4, tissue histogram features are

$$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$5

and tissue co-occurrence features are

$$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$6

A boundary token BD is introduced for neighbors that fall outside $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$7, which highlights tissue transitions at duct borders; SHAP identifies boundary co-occurrences such as BD↔BE and BD↔NC as informative.

Feature extraction proceeds at three levels. Level 1 computes features from duct masks $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$8. Level 2 computes tissue histograms and co-occurrences in bounding-box neighborhoods $$\widetilde{p}_\ell^{(i)} = \alpha\, p_{k1}^{(i)} + (1 - \alpha)\, p_{k2}^{(i)}, \quad \alpha \sim \mathcal{U}[0,1], \quad i=1..E.$$9, thereby incorporating periductal context. Level 3 computes whole-ROI histograms and co-occurrences independent of duct instances. Aggregation across ducts can use mean pooling, max pooling, or score-weighted attention,

$b_\ell$0

The concatenated feature vector is then classified with a classical classifier. The paper compares random forest, MLP with hidden layers $b_\ell$1, and SVMs with polynomial degree $b_\ell$2 and RBF kernels; random forest performs best.

The diagnosis dataset comprises 428 ROIs from 240 breast biopsies with consensus labels from three expert pathologists for benign, atypia, DCIS, and invasive carcinoma. Four-way classification accuracy is $b_\ell$3, matching general pathologists at $b_\ell$4 and outperforming Y-Net at $b_\ell$5, MIL with learned fusion at $b_\ell$6, MIL max-pooling at $b_\ell$7, and semantic learning at $b_\ell$8. Ablations show that using both duct-level instance features and tissue-level semantic features is best at $b_\ell$9, versus tissue-only $J$00 and duct-only $J$01. Co-occurrence features alone reach $J$02, and combining histograms with co-occurrences gives $J$03. Multi-level extraction also improves performance: ROI-only $J$04, duct mask-only $J$05, duct box-only $J$06, and all together $J$07.

Binary tasks further show clinically structured behavior. For invasive versus non-invasive, DIOP reports sensitivity $J$08, specificity $J$09, accuracy $J$10, and F1 $J$11. For atypia/DCIS versus benign, it reports sensitivity $J$12, specificity $J$13, accuracy $J$14, and F1 $J$15. For DCIS versus atypia, it reports sensitivity $J$16, specificity $J$17, accuracy $J$18, and F1 $J$19. The entire stack runs in a few seconds per ROI.

The pipeline’s limitations are mostly structural. Duct masks are silver-standard rather than fully manual. Heavily distorted ducts can be split or merged. In invasive cancer, duct-centric features can underperform because tumor cells escape beyond ducts. The dataset is enriched for atypia and DCIS, and formal significance testing is not reported. Even so, the paper makes clear that HistoPipe can denote an object-centric diagnostic workflow rather than a synthetic-data generator.

4. Whole-slide segmentation, pathomics, and visualization

In the kidney paper (Keller et al., 22 Oct 2025), HistoPipe is presented as a general-purpose whole-slide histology processing blueprint centered on segmentation of functional tissue units, extraction of clinically interpretable pathomics, and web-native visualization. The concrete use case is renal histology from KPMP and HuBMAP.

The input layer supports brightfield RGB WSIs in pyramidal formats supported by OME-TIFF and OME-NGFF, including images embedded inside SpatialData objects. The segmentation layer combines three pretrained model families: Detectron2 panoptic segmentation for glomeruli, tubules, and arteries or arterioles; a U-Net for peritubular capillaries; and DeeplabV2 for IFTA. Inference is performed at WSI scale by chunking or tiling, overlap-aware stitching, connected-components where needed, and postprocessing such as hole filling, boundary smoothing, watershed separation, and panoptic reconciliation across tiles.

The output representation is unusually explicit. HistoPipe stores a multi-channel bitmask image with one channel per observation type. Pixel value $J$20 denotes background; non-zero pixels are $J$21-indexed instance IDs for instance-type channels. Example channels include “glomerulus,” “tubule,” “artery,” “ptc,” and “ifta.” These masks can be written as OME-TIFF or OME-NGFF Labels with multiscales. Per-observation features are stored in AnnData, with adata.obs indexed by the instance IDs that match non-zero pixel values in the corresponding mask channel. Multiple AnnData tables can be bundled in MuData, and images, labels, and tables can be co-registered in SpatialData.

The feature layer emphasizes hand-engineered, clinically interpretable morphometrics. Generic morphology includes area $J$22, perimeter $J$23, centroid, convex area $J$24, major and minor axes, eccentricity, circularity

$J$25

and solidity

$J$26

Kidney-specific features include mesangial area and mesangial fraction for glomeruli, luminal fraction

$J$27

and artery wall thickness

$J$28

as well as wall-to-lumen area ratio

$J$29

Texture features include GLCM-derived Haralick statistics, and distance-transform features include wall or TBM thickness and nearest-neighbor distances between FTUs.

Visualization is built around an extension of Vitessce. The system renders a multiscale RGB image layer together with a multi-channel bitmask overlay driven by a fragment shader. Each channel can be assigned a static color, a set-based categorical color, or a feature-based quantitative colormap. The shader can compute outlines at user-defined stroke width on the fly, so stroked versus filled visualization does not require separate outline masks. Feature views such as histograms and scatterplots consume AnnData tables, and cross-filtering links selections in feature space to spatial highlighting in the WSI view.

The paper reports atlas-scale deployment. As of Jan 8, 2024, 222 PAS-stained WSIs from 112 KPMP biopsies had been processed. The disease categories listed include Acute Interstitial Nephritis, Acute Tubular Injury, Diabetic Kidney Disease, Hypertensive Kidney Disease, and “Other” or “Cannot be determined.” The authors emphasize that visualization is client-side WebGL and does not require specialized viewer servers.

A plausible implication of this formulation is that HistoPipe here acts primarily as a representation and interoperability contract. The central invariant is not a specific neural architecture but the linkage between multiscale images, typed segmentation channels, instance IDs, and per-instance feature tables.

5. Quality control and ROI selection as HistoPipe front ends

Two 2024 papers define QC-oriented entry points for HistoPipe (Patil et al., 2024, Patil et al., 2024). The first introduces HistoROI, a lightweight six-class WSI patch classifier for epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. The second builds a semantic-segmentation QC pipeline for tissue, blur, tissue folds, and pen marks, and explicitly states that annotated images for tissue and blur segmentation were automatically prepared by mosaicking patches whose labels were identified with HistoROI.

HistoROI uses a ResNet-18 classifier on $J$30 patches extracted at $J$31 magnification, approximately $J$32. Background exclusion discards patches if more than $J$33 of pixels have mean RGB greater than $J$34. The training set contains 2,169,355 patches from 50 BRIGHT WSIs, with class counts of 787,168 epithelium, 863,989 stroma, 98,293 lymphocytes, 245,525 adipose, 127,393 artifacts, and 46,987 miscellaneous. Human-in-the-loop labeling uses EfficientNet-b0 block-2 embeddings pooled to 40 dimensions, k-means with $J$35 per WSI, and 25 patches per cluster displayed as a $J$36 grid. Heterogeneous clusters are re-clustered once, and each WSI can be annotated in less than 15 minutes. The workflow expands over three rounds from 20 to 50 WSIs by manually selecting out-of-distribution slides with poor predictions.

The reported generalization results are concrete. On CRC-100k, HistoROI reaches $J$37 overall accuracy, with LYM, ADI, and BACK mapped correctly at more than $J$38. On TCGA-4Org artifact and foreground QC, it outperforms HistoQC on 65 of 93 WSIs, with mean Dice $J$39 versus $J$40. As a prefilter for downstream MIL, it improves CAMELYON metastasis detection AUC from $J$41 to $J$42 in the median, and from $J$43 to $J$44 in the mean, using QCFat-- bagging; on TCGA-Lung adenocarcinoma versus squamous-cell carcinoma classification, test-time filtering improves AUC from $J$45 to $J$46.

The semantic-segmentation QC pipeline in (Patil et al., 2024) partitions the problem into four subtasks with lightweight models. Tissue segmentation uses a UNet with ResNet18 encoder on $J$47 inputs at $J$48, with classes foreground tissue, adipose, and background. Blur segmentation uses a UNet with ResNet18 encoder on $J$49 inputs at $J$50, with 8 ordinal classes from 0 to 7; blur is synthesized at $J$51 and downsampled to $J$52. Tissue folds use another UNet with ResNet18 encoder at $J$53. Pen marks use UNet++ with ResNet34 encoder on $J$54 thumbnails. Training uses collage generation: tissue collages are $J$55 grids of $J$56 cells, and blur collages are $J$57 grids populated with synthetically blurred patches. Pen marks are manually annotated on 350 TCGA WSIs; tissue folds are annotated on 250 BRIGHT patches.

The evaluation spans more than 11,000 TCGA WSIs from 28 organs. The selected models achieve tissue Dice $J$58, blur AUC-ROC $J$59, tissue-fold Dice $J$60, and pen-mark Dice $J$61. Average inference times per WSI are 97.06 s for tissue segmentation, 363.56 s for blur segmentation, 384.66 s for tissue folds, and 5.62 s for pen marks, for an approximate serial throughput of about 850 s per WSI, or about 4.2 WSIs per hour per GPU if tasks are run sequentially. In discordant cases against HistoQC, pathologists preferred the new pipeline’s masks in the majority of sampled cases, including 16 of 20 in the Dice $J$62–$J$63 bucket and 14 of 20 in the Dice $J$64–$J$65 bucket.

These QC papers broaden the meaning of HistoPipe from synthesis or diagnosis to cohort curation and artifact-aware region selection. The common output is not a diagnosis or a synthetic image but a set of masks or patch lists that can gate downstream MIL, segmentation, or morphometry. The limitations are also explicit: HistoROI can under-detect pen marks because such examples were scarce in training; single-label patches are problematic for mixed-content tiles; the segmentation QC pipeline can confuse dark, out-of-focus regions with folds and can flag thyroid colloid or cartilage as blur.

6. Related formulations, homonymous reuse, and conceptual significance

A 2019 whole-brain histology-to-MRI registration study describes a computational pipeline that the authors characterize as matching what a HistoPipe would comprise (Alegro et al., 2019). Its modules are strongly pipeline-like: RGB-to-YIQ conversion; 2-component GMM segmentation on $J$66; optional Chan–Vese refinement; robust symmetric 2D affine registration on entropy images via FreeSurfer; optional 2D SyN with Mattes mutual information; stacking by blockface geometry to avoid slice-to-slice drift; inter-slice histogram matching; MRI preprocessing with N3, BET, and upsampling to $J$67 mm isotropic; and 3D SyN for cross-modality registration. In two brains, the reported overall mean Dice is $J$68, overall mean nWSD is $J$69, and Jacobian determinants are non-negative. This usage is important because it shows that the HistoPipe pattern also encompasses registration workflows with explicit preprocessing, transform reuse, and QC.

A separate 2025 systems paper reuses the name outside histology (He et al., 26 Aug 2025). In RhymeRL, HistoPipe is a two-tier, history-driven rollout scheduling strategy for LLM reinforcement learning. Tier-1 sorts prompts by previous-epoch rollout lengths into equal-sized ranking groups and alternates ascending and descending assignment across odd and even steps to create inter-step complementarity. Tier-2 allocates different numbers of data-parallel rollout workers per group so that group completion times approximate

$J$70

with feasibility determined from a profiled execution-time model $J$71. The scheduler contributes $J$72 end-to-end throughput on math-14B and $J$73 on code-14B relative to a strong disaggregated baseline, with Tier-2 adding a further $J$74 and $J$75, respectively; the full RhymeRL system reports a $J$76 performance improvement over existing methods. This is a genuine homonym rather than a histopathology pipeline.

Taken together, the cited literature suggests several stable design principles behind the histology uses of HistoPipe. First, the workflow is modular and stagewise: shape synthesis, placement, and rendering in (Basla et al., 2024); instance segmentation, tissue segmentation, feature aggregation, and classification in (Li et al., 2020); segmentation, feature extraction, packaging, and visualization in (Keller et al., 22 Oct 2025); QC gating before downstream learning in (Patil et al., 2024, Patil et al., 2024); and preprocessing, 2D alignment, 3D reconstruction, and cross-modality registration in (Alegro et al., 2019). Second, intermediate artifacts are first-class objects: $J$77, $J$78, multi-channel bitmasks with 1-indexed IDs, AnnData tables, and QC masks. Third, expert knowledge enters explicitly through priors $J$79 and $J$80, weak or dense annotations, review interfaces such as QuPath or Vitessce, and task-specific postprocessing rules.

The main misconception to avoid is that HistoPipe names one fixed algorithm. In the cited papers, it names a family of modular, expert-steerable workflows for histological computation, while one later paper uses the same name for an unrelated scheduler in LLM RL.