The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come.

https://hal.inria.fr/inria-00615100/document

Medical image analysis: progress over two decades and the challenges ahead

https://bio3d.colorado.edu/SerialEM/serialEMJSB.pdf

Automated electron microscope tomography using robust prediction of specimen movements.

中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

http://www.cvs.rochester.edu/knill_lab/papers/35_TINS_2004.pdf

The Bayesian brain: the role of uncertainty in neural coding and computation

Neural decoding of semantic processing during the attentional blink

Radiologists must classify and interpret medical images on the basis of visual inspection We examined how the perception of radiological scans might be affected by common processes of adaptation in the visual system Adaptation selectively adjusts sensitivity to the properties of the stimulus in current view, inducing an aftereffect in the appearance of stimuli viewed subsequently These perceptual changes have been found to affect many visual attributes, but whether they are relevant to medical image perception is not well understood To examine this we tested whether aftereffects could be generated by the characteristic spatial structure of radiological scans, and whether this could bias their appearance along dimensions that are routinely used to classify them Measurements were focused on the effects of adaptation to images of normal mammograms, and were tested in observers who were not radiologists Tissue density in mammograms is evaluated visually and ranges from "dense" to "fatty" Arrays of images varying in intermediate levels between these categories were created by blending dense and fatty images with different weights Observers first adapted by viewing image samples of dense or fatty tissue, and then judged the appearance of the intermediate images by using a texture matching task This revealed pronounced perceptual aftereffects - prior exposure to dense images caused an intermediate image to appear more fatty and vice versa Moreover, the appearance of the adapting images themselves changed with prolonged viewing, so that they became less distinctive as textures These aftereffects could not be accounted for by the contrast differences or power spectra of the images, and instead tended to follow from the phase spectrum Our results suggest that observers can selectively adapt to the properties of radiological images, and that this selectivity could strongly impact the perceived textural characteristics of the images

Adaptation aftereffects in the perception of radiological images

We measure the detection and discrimination efficiencies of conventional power-Doppler estimation of perfusion without contrast enhancement. The measurements are made in a phantom with known blood-mimicking fluid flow rates in the presence of clutter and noise. Efficiency is measured by comparing functions of the areas under the receiver operating characteristic curve for Doppler estimators with those of the ideal discriminator, for which we estimate the temporal covariance matrix from echo data. Principal-component analysis is examined as a technique for increasing the accuracy of covariance matrices estimated from echo data. We find that Doppler estimators are <50% efficient at directed perfusion detection between 0.1 and 2.0 mL/min per 2 cm2 flow area. The efficiency was 20%–40% for the task of discriminating between two perfusion rates in the same range. We conclude that there are reasons to search for more efficient perfusion estimators, one that incorporates covariance matrix information that could significantly enhance the utility of Doppler ultrasound without contrast enhancement.

Efficiency of U.S. Tissue Perfusion Estimators

The statistical efficiency of human observers in diagnostic tasks is an important measure of how effectively task relevant information in the image is being utilized. Most efficiency studies have investigated efficiency in terms of contrast or size effects. In many cases, malignant lesions will have similar contrast to normal or benign objects, but can be distinguished by properties of their boundary. We investigate this issue in the framework of malignant/benign discrimination tasks for the breast with ultrasound. In order to identify effects in terms of specific features and to control for other effects such as aberration or specular reflections, we simulate the formation of beam-formed radio-frequency (RF) data. We consider three tasks related to lesion boundaries including boundary eccentricity, boundary sharpness, 
and detection of boundary spiculations. We also consider standard detection and contrast discrimination tasks. We find that human observers exhibit surprisingly low efficiency with respect to the Ideal observer acting on RF data in boundary discrimination tasks (0.08%-3.3%), and that efficiency of human observers is substantially increased by Wiener-filtering RF frame data. We also find a limitation in efficiency is the computation of an envelope image from the RF data recorded by the transducer. Approximations to the Ideal observer acting on the envelope images indicate that humans may be substantially more efficient (10%-75%) with respect to the envelope Ideal observers. Our work suggests that significant diagnostic information may be lost in standard envelope processing in the formation of ultrasonic images.

Observer efficiency in boundary discrimination tasks related to assessment of breast lesions with ultrasound

Odour cues associated with shifts in ovarian hormones indicate ovulatory timing in females of many nonhuman species. Although prior evidence supports women's body odours smelling more attractive on days when conception is possible, that research has left ambiguous how diagnostic of ovulatory timing odour cues are, as well as whether shifts in odour attractiveness are correlated with shifts in ovarian hormones. Here, 46 women each provided six overnight scent and corresponding day saliva samples spaced five days apart, and completed luteinizing hormone tests to determine ovulatory timing. Scent samples collected near ovulation were rated more attractive, on average, relative to samples from the same women collected on other days. Importantly, however, signal detection analyses showed that rater discrimination of fertile window timing from odour attractiveness ratings was very poor. Within-women shifts in salivary oestradiol and progesterone were not significantly associated with within-women shifts in odour attractiveness. Between-women, mean oestradiol was positively associated with mean odour attractiveness. Our findings suggest that raters cannot reliably detect women's ovulatory timing from their scent attractiveness. The between-women effect of oestradiol raises the possibility that women's scents provide information about overall cycle fecundity, though further research is necessary to rigorously investigate this possibility.

Craig K. Abbey

Papers

Neural decoding of semantic processing during the attentional blink

Adaptation aftereffects in the perception of radiological images

Efficiency of U.S. Tissue Perfusion Estimators

Observer efficiency in boundary discrimination tasks related to assessment of breast lesions with ultrasound

Does scent attractiveness reveal women's ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odour attractiveness