{"id":"24b8cc94-881c-4939-bacd-4d1b65f6f909","arxiv_id":"2604.11927","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"A workflow generates consistent dense tissue masks in DBT volumes by annotating only the central slice, projecting the ROI, and iteratively adjusting per-slice thresholds, yielding median Dice scores of 0.83 against manual segmentations.","lead":"This paper introduces a semi-automated workflow for creating dense tissue segmentation masks in digital breast tomosynthesis by requiring human annotation only on the central slice of each volume. The approach could accelerate the creation of training datasets needed for AI models that estimate personalized breast cancer risk from tissue density.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Evaluation only on 20th/80th percentile slices leaves full-volume consistency untested","rationale":"The reader's weakest assumption (projection + iterative adjustment without major non-central errors) is exactly the load-bearing point. The restricted slice sampling directly tests whether that assumption holds; the abstract-only review already flagged missing implementation details, so the evaluation gap reinforces rather than alters the UNVERDICTED verdict.","tokens_in":1747,"tokens_out":305,"duration_ms":48968,"concrete_test":"For the 44 volumes, manually segment the 10th and 90th percentile slices (additional 88 slices) and recompute Dice against the framework outputs; if the new median falls below 0.75 or if >20% of slices show Dice <0.70, the full-volume accuracy claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that central-slice ROI projection plus iterative per-slice threshold adjustment produces accurate dense-tissue masks on every slice. Validation, however, is confined to Dice scores against manual segmentations on only the 20th and 80th percentile slices (176 slices total). No quantitative checks are reported for edge slices, for boundary smoothness across the stack, or for cases where tissue distribution deviates from the central slice. Because the adjustment rule itself is not specified, it is impossible to determine whether the 0.83 median Dice generalizes or whether systematic errors appear outside the sampled percentiles.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces a workflow for efficiently creating dense tissue ground truth masks in DBT volumes. Users annotate a rough ROI and select a threshold only on the central slice; the method projects this ROI to other slices and iteratively adjusts per-slice thresholds for consistent segmentation. On 44 DBT volumes, it reports a median Dice score of 0.83 against manual annotations on 176 selected slices and 0.84 inter-reader agreement.","tokens_in":1895,"tokens_out":462,"duration_ms":21977,"significance":"If the approach proves robust across entire volumes, it would meaningfully lower the barrier to creating large annotated datasets for training dense tissue segmentation models in DBT, supporting personalized breast cancer risk assessment. The close alignment with inter-reader variability (0.84) is a positive indicator of practical utility, and the direct empirical comparison to manual labels on a non-trivial number of slices provides moderate evidence of feasibility.","major_comments":[{"comment":"Evaluation: Accuracy is assessed only via Dice scores on the 20th and 80th percentile slices (176 slices total across CC/MLO views), with no reported quantitative checks on edge slices, boundary smoothness across the volume, or cases where tissue distribution deviates from the central slice. This sampling directly limits support for the central claim of consistent dense tissue delineations across the full DBT volume.","section":"Evaluation"},{"comment":"Methods: The iterative adjustment algorithm for slice-specific thresholds is described only at a high level without the exact rule, convergence criteria, or handling of variations away from the central slice. This omission prevents assessment of reproducibility and whether the 0.83 median Dice generalizes without systematic errors in non-central slices.","section":"Methods"}],"minor_comments":[{"comment":"Abstract: Inclusion criteria for the 44 DBT volumes from DBTex and the split between CC and MLO views could be stated more explicitly to allow better assessment of generalizability.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The limited slice sampling in validation is a load-bearing concern for the workflow's claimed efficiency and accuracy on full volumes; this should be addressed before acceptance."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive and insightful comments on our manuscript. We address each major comment point by point below, providing clarifications and indicating the revisions we will make to improve the paper.","responses":[{"response":"We agree that evaluating only the 20th and 80th percentile slices provides limited direct quantitative evidence for consistency on edge slices or in cases of atypical tissue distribution, and that this constrains the strength of the claim for full-volume consistency. The sampling strategy was chosen because full manual annotation of entire volumes is prohibitively time-consuming (precisely the problem our workflow addresses), while the 20th/80th percentiles still represent slices distant from the central annotated slice. The median Dice of 0.83 being nearly identical to inter-reader agreement (0.84) offers indirect support for practical utility. In the revised manuscript we will (i) explicitly acknowledge this as a limitation in the Discussion, (ii) add qualitative visualizations of full-volume segmentations across multiple cases to demonstrate boundary smoothness and handling of edge slices, and (iii) report quantitative Dice scores on edge slices for a small additional subset of volumes if radiologist time permits.","revision_made":"partial","referee_comment":"[Evaluation] Evaluation: Accuracy is assessed only via Dice scores on the 20th and 80th percentile slices (176 slices total across CC/MLO views), with no reported quantitative checks on edge slices, boundary smoothness across the volume, or cases where tissue distribution deviates from the central slice. This sampling directly limits support for the central claim of consistent dense tissue delineations across the full DBT volume."},{"response":"We accept this criticism; the original Methods section intentionally kept the description concise but thereby omitted necessary implementation details. In the revision we will expand the Methods to include: the precise iterative rule (how the per-slice threshold is updated based on projected ROI statistics), the convergence criterion (e.g., change in segmented area or intensity threshold below a fixed epsilon), and explicit handling of tissue-distribution deviations (e.g., fallback to neighboring-slice thresholds or manual override). We will also supply pseudocode for the full procedure to enable reproducibility.","revision_made":"yes","referee_comment":"[Methods] Methods: The iterative adjustment algorithm for slice-specific thresholds is described only at a high level without the exact rule, convergence criteria, or handling of variations away from the central slice. This omission prevents assessment of reproducibility and whether the 0.83 median Dice generalizes without systematic errors in non-central slices."}],"tokens_in":1415,"tokens_out":541,"duration_ms":39759,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing here is a practical workflow that lets a user draw a rough ROI on the central DBT slice, pick a threshold, and then project it while tweaking per-slice thresholds to generate dense tissue masks across the volume. They report a median Dice of 0.83 against manual labels on 176 slices and 0.84 between two radiologists, using 44 volumes from the DBTex dataset. This directly targets the shortage of labeled data for breast density models in tomosynthesis, which matters for risk estimation work in oncology imaging. The approach is a straightforward extension of existing semi-automated segmentation ideas, applied to the slice-by-slice nature of DBT reconstructions, and the inter-reader baseline is a sensible check. It does well at showing how central-slice focus can reduce effort without obvious collapse in the tested cases. The soft spots are clear and worth noting. Validation stays limited to the 20th and 80th percentile slices, so there is no direct evidence on edge slices or full-volume consistency when tissue patterns shift away from the center. The iterative threshold adjustment rule itself is not described in enough detail to judge reproducibility or failure cases. The dataset is modest, which fits an early practical paper but leaves broader claims thin. This is for researchers building or training segmentation models in breast imaging who need faster ground-truth generation. It is not a theoretical advance, but the problem it solves is real enough that a serious referee could usefully push for fuller evaluation details and clearer algorithm steps. I would send it to peer review rather than desk reject.","headline":"The workflow cuts annotation labor for DBT dense tissue masks by limiting manual work to the central slice and achieves reasonable Dice scores, but validation is restricted to only the 20th and 80th percentile slices.","tokens_in":2411,"tokens_out":398,"would_cite":false,"duration_ms":20384,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Annotating only the central slice generates accurate dense tissue masks for entire DBT volumes","keywords":["digital breast tomosynthesis","dense tissue segmentation","ground truth generation","semi-automated annotation","fibroglandular tissue","DBT","Dice similarity coefficient"],"falsifier":"Full manual segmentation of every slice in a new set of DBT volumes, followed by calculation of Dice scores between those complete labels and the workflow outputs to check whether agreement stays near 0.83 or falls sharply away from the center.","tokens_in":2674,"feed_emoji":"🩺","tokens_out":710,"duration_ms":30295,"temperature":0.7,"pith_summary":"The paper introduces a workflow that lets users create binary masks of fibroglandular dense tissue in digital breast tomosynthesis by annotating only the central slice of each volume. A user draws a rough region of interest around the dense tissue and picks a threshold on that middle slice. The algorithm projects the region to every other slice and iteratively tweaks the threshold per slice to keep the masks consistent through the full 3D volume. This approach cuts annotation time and labor while still producing results that match radiologist manual segmentations at a median Dice score of 0.83 across 176 slices from 44 volumes. The method targets the shortage of labeled data that currently limits development of models for personalized breast cancer risk estimation.","feed_headline":"Central-slice annotation alone yields full DBT dense tissue masks","feed_subtitle":"Rough central ROI projects across the volume with per-slice threshold tweaks and reaches 0.83 median Dice to manual labels","key_machinery":"Projection of a central-slice rough ROI combined with iterative per-slice threshold adjustment, which propagates the initial annotation while enforcing consistency across the reconstructed volume.","core_discovery":"The framework enables a user to outline a rough ROI enclosing dense tissue on the central reconstructed slice of a DBT volume and select a segmentation threshold. The algorithm then projects the ROI to the remaining slices and iteratively adjusts slice-specific thresholds to maintain consistent dense tissue delineation across the DBT volume. Evaluation on 44 volumes from the DBTex dataset yields a median Dice score of 0.83 against manual segmentations on 176 slices and shows inter-reader agreement of 0.84.","pith_inferences":["The same central-slice projection idea could transfer to other volumetric medical imaging tasks that need tissue masks.","Integration into annotation software would let radiologists generate usable ground truth far faster than full manual outlining.","Further automation of the initial threshold selection step could reduce user involvement even more.","Testing the workflow on broader screening populations would reveal whether slice-to-slice consistency holds outside the current evaluation set."],"forward_implications":["Annotation labor is confined to a single central slice rather than the entire stack of slices.","The generated masks reach a median Dice overlap of 0.83 with expert manual labels.","This performance level is comparable to the 0.84 median Dice agreement observed between two radiologists.","Much larger collections of labeled DBT data become practical to produce for training segmentation algorithms."],"fun_headline_variants":["Central slice ROI projects to generate DBT dense tissue masks","Annotate only central DBT slice for full volume tissue masks","Projecting central annotation produces consistent DBT tissue masks","Single central outline with threshold tweaks masks all DBT slices","Central DBT annotation projects volume-wide with slice thresholds"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Projecting the central-slice ROI and iteratively adjusting thresholds slice by slice will produce accurate dense tissue boundaries on non-central slices without introducing major errors or inconsistencies.","fun_headline_variants_meta":{"raw":{"variants":["Central slice ROI projects to generate DBT dense tissue masks","Annotate only central DBT slice for full volume tissue masks","Projecting central annotation produces consistent DBT tissue masks","Single central outline with threshold tweaks masks all DBT slices","Central DBT annotation projects volume-wide with slice thresholds"]},"model":"grok-4.3","cost_usd":0.007587,"raw_usage":{"total_tokens":3505,"prompt_tokens":725,"num_sources_used":0,"completion_tokens":70,"cost_in_usd_ticks":75874500,"prompt_tokens_details":{"text_tokens":725,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2710,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":725,"tokens_out":70,"duration_ms":25273,"temperature":1.0,"reasoning_tokens":2710,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-10T16:33:00.119560+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Full manual segmentation of every slice in a new set of DBT volumes, followed by calculation of Dice scores between those complete labels and the workflow outputs to check whether agreement stays near 0.83 or falls sharply away from the center.","supporting_citations":[],"review_version":1}