5 research outputs found
Strategising template-guided needle placement for MR-targeted prostate biopsy
Clinically significant prostate cancer has a better chance to be sampled
during ultrasound-guided biopsy procedures, if suspected lesions found in
pre-operative magnetic resonance (MR) images are used as targets. However, the
diagnostic accuracy of the biopsy procedure is limited by the
operator-dependent skills and experience in sampling the targets, a sequential
decision making process that involves navigating an ultrasound probe and
placing a series of sampling needles for potentially multiple targets. This
work aims to learn a reinforcement learning (RL) policy that optimises the
actions of continuous positioning of 2D ultrasound views and biopsy needles
with respect to a guiding template, such that the MR targets can be sampled
efficiently and sufficiently. We first formulate the task as a Markov decision
process (MDP) and construct an environment that allows the targeting actions to
be performed virtually for individual patients, based on their anatomy and
lesions derived from MR images. A patient-specific policy can thus be
optimised, before each biopsy procedure, by rewarding positive sampling in the
MDP environment. Experiment results from fifty four prostate cancer patients
show that the proposed RL-learned policies obtained a mean hit rate of 93% and
an average cancer core length of 11 mm, which compared favourably to two
alternative baseline strategies designed by humans, without hand-engineered
rewards that directly maximise these clinically relevant metrics. Perhaps more
interestingly, it is found that the RL agents learned strategies that were
adaptive to the lesion size, where spread of the needles was prioritised for
smaller lesions. Such a strategy has not been previously reported or commonly
adopted in clinical practice, but led to an overall superior targeting
performance when compared with intuitively designed strategies.Comment: Paper submitted and accepted to CaPTion (Cancer Prevention through
early detecTion) @ MICCAI 2022 Worksho
Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study
Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis
is the efficient use of the support image data, which are labelled to classify
or segment new classes, a task that otherwise requires substantially more
training images and expert annotations. This work describes a fully 3D
prototypical few-shot segmentation algorithm, such that the trained networks
can be effectively adapted to clinically interesting structures that are absent
in training, using only a few labelled images from a different institute.
First, to compensate for the widely recognised spatial variability between
institutions in episodic adaptation of novel classes, a novel spatial
registration mechanism is integrated into prototypical learning, consisting of
a segmentation head and an spatial alignment module. Second, to assist the
training with observed imperfect alignment, support mask conditioning module is
proposed to further utilise the annotation available from the support images.
Extensive experiments are presented in an application of segmenting eight
anatomical structures important for interventional planning, using a data set
of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results
demonstrate the efficacy in each of the 3D formulation, the spatial
registration, and the support mask conditioning, all of which made positive
contributions independently or collectively. Compared with the previously
proposed 2D alternatives, the few-shot segmentation performance was improved
with statistical significance, regardless whether the support data come from
the same or different institutes.Comment: accepted by Medical Image Analysi
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis is the
efficient use of the support image data, which are labelled to classify or segment new
classes, a task that otherwise requires substantially more training images and expert
annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting
structures that are absent in training, using only a few labelled images from a different
institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism
is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment,
support mask conditioning module is proposed to further utilise the annotation available
from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data
set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results
demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the
support mask conditioning, all of which made positive contributions independently or
collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether
the support data come from the same or different institutes