23 research outputs found
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Background: View planning for the acquisition of cardiac magnetic resonance
(CMR) imaging remains a demanding task in clinical practice. Purpose: Existing
approaches to its automation relied either on an additional volumetric image
not typically acquired in clinic routine, or on laborious manual annotations of
cardiac structural landmarks. This work presents a clinic-compatible,
annotation-free system for automatic CMR view planning. Methods: The system
mines the spatial relationship, more specifically, locates the intersecting
lines, between the target planes and source views, and trains deep networks to
regress heatmaps defined by distances from the intersecting lines. The
intersection lines are the prescription lines prescribed by the technologists
at the time of image acquisition using cardiac landmarks, and retrospectively
identified from the spatial relationship. As the spatial relationship is
self-contained in properly stored data, the need for additional manual
annotation is eliminated. In addition, the interplay of multiple target planes
predicted in a source view is utilized in a stacked hourglass architecture to
gradually improve the regression. Then, a multi-view planning strategy is
proposed to aggregate information from the predicted heatmaps for all the
source views of a target plane, for a globally optimal prescription, mimicking
the similar strategy practiced by skilled human prescribers. Results: The
experiments include 181 CMR exams. Our system yields the mean angular
difference and point-to-plane distance of 5.68 degrees and 3.12 mm,
respectively. It not only achieves superior accuracy to existing approaches
including conventional atlas-based and newer deep-learning-based in prescribing
the four standard CMR planes but also demonstrates prescription of the first
cardiac-anatomy-oriented plane(s) from the body-oriented scout.Comment: Medical Physics. arXiv admin note: text overlap with arXiv:2109.1171
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Chest X-ray (CXR) anatomical abnormality detection aims at localizing and
characterising cardiopulmonary radiological findings in the radiographs, which
can expedite clinical workflow and reduce observational oversights. Most
existing methods attempted this task in either fully supervised settings which
demanded costly mass per-abnormality annotations, or weakly supervised settings
which still lagged badly behind fully supervised methods in performance. In
this work, we propose a co-evolutionary image and report distillation (CEIRD)
framework, which approaches semi-supervised abnormality detection in CXR by
grounding the visual detection results with text-classified abnormalities from
paired radiology reports, and vice versa. Concretely, based on the classical
teacher-student pseudo label distillation (TSD) paradigm, we additionally
introduce an auxiliary report classification model, whose prediction is used
for report-guided pseudo detection label refinement (RPDLR) in the primary
vision detection task. Inversely, we also use the prediction of the vision
detection model for abnormality-guided pseudo classification label refinement
(APCLR) in the auxiliary report classification task, and propose a co-evolution
strategy where the vision and report models mutually promote each other with
RPDLR and APCLR performed alternatively. To this end, we effectively
incorporate the weak supervision by reports into the semi-supervised TSD
pipeline. Besides the cross-modal pseudo label refinement, we further propose
an intra-image-modal self-adaptive non-maximum suppression, where the pseudo
detection labels generated by the teacher vision model are dynamically
rectified by high-confidence predictions by the student. Experimental results
on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to
several up-to-date weakly and semi-supervised methods
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Unsupervised domain adaption has been widely adopted in tasks with scarce
annotated data. Unfortunately, mapping the target-domain distribution to the
source-domain unconditionally may distort the essential structural information
of the target-domain data, leading to inferior performance. To address this
issue, we firstly propose to introduce active sample selection to assist domain
adaptation regarding the semantic segmentation task. By innovatively adopting
multiple anchors instead of a single centroid, both source and target domains
can be better characterized as multimodal distributions, in which way more
complementary and informative samples are selected from the target domain. With
only a little workload to manually annotate these active samples, the
distortion of the target-domain distribution can be effectively alleviated,
achieving a large performance gain. In addition, a powerful semi-supervised
domain adaptation strategy is proposed to alleviate the long-tail distribution
problem and further improve the segmentation performance. Extensive experiments
are conducted on public datasets, and the results demonstrate that the proposed
approach outperforms state-of-the-art methods by large margins and achieves
similar performance to the fully-supervised upperbound, i.e., 71.4% mIoU on
GTA5 and 71.8% mIoU on SYNTHIA. The effectiveness of each component is also
verified by thorough ablation studies.Comment: Accepted by TPAMI-IEEE Transactions on Pattern Analysis and Machine
Intelligence. arXiv admin note: substantial text overlap with
arXiv:2108.0801