In recent years, explainable methods for artificial intelligence (XAI) have
tried to reveal and describe models' decision mechanisms in the case of
classification tasks. However, XAI for semantic segmentation and in particular
for single instances has been little studied to date. Understanding the process
underlying automatic segmentation of single instances is crucial to reveal what
information was used to detect and segment a given object of interest. In this
study, we proposed two instance-level explanation maps for semantic
segmentation based on SmoothGrad and Grad-CAM++ methods. Then, we investigated
their relevance for the detection and segmentation of white matter lesions
(WML), a magnetic resonance imaging (MRI) biomarker in multiple sclerosis (MS).
687 patients diagnosed with MS for a total of 4043 FLAIR and MPRAGE MRI scans
were collected at the University Hospital of Basel, Switzerland. Data were
randomly split into training, validation and test sets to train a 3D U-Net for
MS lesion segmentation. We observed 3050 true positive (TP), 1818 false
positive (FP), and 789 false negative (FN) cases. We generated instance-level
explanation maps for semantic segmentation, by developing two XAI methods based
on SmoothGrad and Grad-CAM++. We investigated: 1) the distribution of gradients
in saliency maps with respect to both input MRI sequences; 2) the model's
response in the case of synthetic lesions; 3) the amount of perilesional tissue
needed by the model to segment a lesion. Saliency maps (based on SmoothGrad) in
FLAIR showed positive values inside a lesion and negative in its neighborhood.
Peak values of saliency maps generated for these four groups of volumes
presented distributions that differ significantly from one another, suggesting
a quantitative nature of the proposed saliency. Contextual information of 7mm
around the lesion border was required for their segmentation.

本研究提出了两种基于 SmoothGrad 和 Grad-CAM++ 方法的实例级解释映射，用于语义分割的高可解释性人工智能模型，通过对白质病变的磁共振成像进行检测和分割，揭示了这些方法的相关性，以及分割病变所需的周围组织信息。

多发性硬化症损害分割中的实例级定量显著性

Instance-level quantitative saliency in multiple sclerosis lesion  segmentation

Here we present a method for the simultaneous segmentation of white matter
lesions and normal-appearing neuroanatomical structures from multi-contrast
brain MRI scans of multiple sclerosis patients. The method integrates a novel
model for white matter lesions into a previously validated generative model for
whole-brain segmentation. By using separate models for the shape of anatomical
structures and their appearance in MRI, the algorithm can adapt to data
acquired with different scanners and imaging protocols without retraining. We
validate the method using four disparate datasets, showing robust performance
in white matter lesion segmentation while simultaneously segmenting dozens of
other brain structures. We further demonstrate that the contrast-adaptive
method can also be safely applied to MRI scans of healthy controls, and
replicate previously documented atrophy patterns in deep gray matter structures
in MS. The algorithm is publicly available as part of the open-source
neuroimaging package FreeSurfer.

提出了一种对多发性硬化患者进行多序列脑 MRI 扫描的白质病变和正常神经解剖结构同时分割的方法，并通过四个不同的数据集验证了该方法的高鲁棒性。

多发性硬化全脑与病变同时分割对比适应方法

A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion  Segmentation in Multiple Sclerosis

Segmentation of white matter lesions and deep grey matter structures is an
important task in the quantification of magnetic resonance imaging in multiple
sclerosis. In this paper we explore segmentation solutions based on
convolutional neural networks (CNNs) for providing fast, reliable segmentations
of lesions and grey-matter structures in multi-modal MR imaging, and the
performance of these methods when applied to out-of-centre data.
We trained two state-of-the-art fully convolutional CNN architectures on the
2016 MSSEG training dataset, which was annotated by seven independent human
raters: a reference implementation of a 3D Unet, and a more recently proposed
3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger
dataset from a single centre, with and without labels for other brain
structures. We quantified changes in performance owing to dataset shift, and
changes in performance by adding the additional brain-structure labels. We also
compared performance with freely available reference methods.
Both fully-convolutional CNN methods substantially outperform other
approaches in the literature when trained and evaluated in cross-validation on
the MSSEG dataset, showing agreement with human raters in the range of human
inter-rater variability. Both architectures showed drops in performance when
trained on single-centre data and tested on the MSSEG dataset. When trained
with the addition of weak anatomical labels derived from Freesurfer, the
performance of the 3D Unet degraded, while the performance of the DeepSCAN net
improved. Overall, the DeepSCAN network predicting both lesion and anatomical
labels was the best-performing network examined.

本文探讨基于卷积神经网络（CNN）的分割解决方案，以提供多模式磁共振成像的快速，可靠的病变和灰质结构分割，重点研究了基于 2016 MSSEG 训练数据集上的两个完全卷积 CNN 架构的性能，并比较了其他方法的性能表现。