Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

Abstract

Abstract. Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation. Under a reasonable transformation function, our approach can be factorized into two stages, and each stage can be efficiently optimized via gradient back-propagation throughout the deep networks. We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation. Without human assistance, our approach reports a 63.44% average accuracy, measured by the Dice-Sørensen coefficient (DSC), which is higher than the number (60.46%) without deep supervision.

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

“…We believe that our algorithm can achieve an even higher accuracy if a more powerful network structure is used. Meanwhile, our approach can be applied to other small organs, e.g., spleen, duodenum or a lesion area in pancreas [13]. In the future, we will try to incorporate the fixedpoint model into an end-to-end learning framework.…”

“…This paper focuses on an important prerequisite of CAD [3] [13], namely, automatic segmentation of small organs (e.g., the pancreas) from CT-scanned images. The difficulty mainly comes from the high anatomical variability and/or the small volume of the target organs.…”

“…In comparison, [8] reports 34.11% DSC on the worst case and [9] reports 23.99%. Meanwhile, our approach can be applied to segmenting other organs or tissues, especially when the target is very small, e.g., the pancreatic cyst [13].…”

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

“…However, there are several challenges for applying existing segmentation algorithms to dual-phase images. First, segmentation of pancreatic lesion, e.g., cysts [17], is more difficult than organ segmentation due to its smaller sizes, lower contrast and texture similarity, etc. Secondly, these algorithms are optimized for segmenting only one type of input, and therefore cannot be directly applied to handle multi-phase data.…”

Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation

“…As shown in Fig. 3, DN sequentially down-samples stages, leading to semantically richer but spatially coarser features [8]. UN follows an encoder-decoder architecture, with the encoder part being a DN and the decoder part an up-sampling network that gradually restores resolution via 2×2 transposed convolution [9].…”