Persistent homology for fast tumor segmentation in whole slide histology images

Automated tumor segmentation in Hematoxylin & Eosin stained histology images is an essential step towards a computer-aided diagnosis system. In this work we propose a novel tumor segmentation approach for a histology whole-slide image (WSI) by exploring the degree of connectivity among nuclei using the novel idea of persistent homology profiles. Our approach is based on 3 steps: 1) selection of exemplar patches from the training dataset using convolutional neural networks (CNNs); 2) construction of persistent homology profiles based on topological features; 3) classification using variant of k-nearest neighbors (k-NN). Extensive experimental results favor our algorithm over a conventional CNN

Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features

Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on the selection of exemplar image patches from a convolution neural network (CNN) and patch classification by quantifying the divergence between the PHPs of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperforms competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.As a result, 79 GRADE-based recommendations, 5 Good Practice Statements (GPS), 18 expert consensuses, 27 answers to background questions (BQs), and summaries of definitions and diagnosis of sepsis were created as responses to 118 CQs. We also incorporated visual information for each CQ according to the time course of treatment, and we will also distribute this as an app. The J-SSCG 2020 is expected to be widely used as a useful bedside guideline in the field of sepsis treatment both in Japan and overseas involving multiple disciplines.other authors: Satoru Hashimoto,Daisuke Hasegawa,Junji Hatakeyama,Naoki Hara,Naoki Higashibeppu,Nana Furushima,Hirotaka Furusono,Yujiro Matsuishi,Tasuku Matsuyama,Yusuke Minematsu,Ryoichi Miyashita,Yuji Miyatake,Megumi Moriyasu,Toru Yamada,Hiroyuki Yamada,Ryo Yamamoto,Takeshi Yoshida,Yuhei Yoshida,Jumpei Yoshimura,Ryuichi Yotsumoto,Hiroshi Yonekura,Takeshi Wada,Eizo Watanabe,Makoto Aoki,Hideki Asai,Takakuni Abe,Yutaka Igarashi,Naoya Iguchi,Masami Ishikawa,Go Ishimaru,Shutaro Isokawa,Ryuta Itakura,Hisashi Imahase,Haruki Imura,Takashi Irinoda,Kenji Uehara,Noritaka Ushio,Takeshi Umegaki,Yuko Egawa,Yuki Enomoto,Kohei Ota,Yoshifumi Ohchi,Takanori Ohno,Hiroyuki Ohbe,Kazuyuki Oka,Nobunaga Okada,Yohei Okada,Hiromu Okano,Jun Okamoto,Hiroshi Okuda,Takayuki Ogura,Yu Onodera,Yuhta Oyama,Motoshi Kainuma,Eisuke Kako,Masahiro Kashiura,Hiromi Kato,Akihiro Kanaya,Tadashi Kaneko,Keita Kanehata,Ken-ichi Kano,Hiroyuki Kawano,Kazuya Kikutani,Hitoshi Kikuchi,Takahiro Kido,Sho Kimura,Hiroyuki Koami,Daisuke Kobashi,Iwao Saiki,Masahito Sakai,Ayaka Sakamoto,Tetsuya Sato,Yasuhiro Shiga,Manabu Shimoto,Shinya Shimoyama,Tomohisa Shoko,Yoh Sugawara,Atsunori Sugita,Satoshi Suzuki,Yuji Suzuki,Tomohiro Suhara,Kenji Sonota,Shuhei Takauji,Kohei Takashima,Sho Takahashi,Yoko Takahashi,Jun Takeshita,Yuuki Tanaka,Akihito Tampo,Taichiro Tsunoyama,Kenichi Tetsuhara,Kentaro Tokunaga,Yoshihiro Tomioka,Kentaro Tomita,Naoki Tominaga,Mitsunobu Toyosaki,Yukitoshi Toyoda,Hiromichi Naito,Isao Nagata,Tadashi Nagato,Yoshimi Nakamura,Yuki Nakamori,Isao Nahara,Hiromu Naraba,Chihiro Narita,Norihiro Nishioka,Tomoya Nishimura,Kei Nishiyama,Tomohisa Nomura,Taiki Haga,Yoshihiro Hagiwara,Katsuhiko Hashimoto,Takeshi Hatachi,Toshiaki Hamasaki,Takuya Hayashi,Minoru Hayashi,Atsuki Hayamizu,Go Haraguchi,Yohei Hirano,Ryo Fujii,Motoki Fujita,Naoyuki Fujimura,Hiraku Funakoshi,Masahito Horiguchi,Jun Maki,Naohisa Masunaga,Yosuke Matsumura,Takuya Mayumi,Keisuke Minami,Yuya Miyazaki,Kazuyuki Miyamoto,Teppei Murata,Machi Yanai,Takao Yano,Kohei Yamada,Naoki Yamada,Tomonori Yamamoto,Shodai Yoshihiro,Hiroshi Tanaka,Osamu NishidaGuideline

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.other authors: Yasuhiro Norisue, Satoru Hashimoto, Daisuke Hasegawa, Junji Hatakeyama, Naoki Hara, Naoki Higashibeppu, Nana Furushima, Hirotaka Furusono, Yujiro Matsuishi, Tasuku Matsuyama, Yusuke Minematsu, Ryoichi Miyashita, Yuji Miyatake, Megumi Moriyasu, Toru Yamada, Hiroyuki Yamada, Ryo Yamamoto, Takeshi Yoshida, Yuhei Yoshida, Jumpei Yoshimura, Ryuichi Yotsumoto, Hiroshi Yonekura, Takeshi Wada, Eizo Watanabe, Makoto Aoki, Hideki Asai, Takakuni Abe, Yutaka Igarashi, Naoya Iguchi, Masami Ishikawa, Go Ishimaru, Shutaro Isokawa, Ryuta Itakura, Hisashi Imahase, Haruki Imura, Takashi Irinoda, Kenji Uehara, Noritaka Ushio, Takeshi Umegaki, Yuko Egawa, Yuki Enomoto, Kohei Ota, Yoshifumi Ohchi, Takanori Ohno, Hiroyuki Ohbe, Kazuyuki Oka, Nobunaga Okada, Yohei Okada, Hiromu Okano, Jun Okamoto, Hiroshi Okuda, Takayuki Ogura, Yu Onodera, Yuhta Oyama, Motoshi Kainuma, Eisuke Kako, Masahiro Kashiura, Hiromi Kato, Akihiro Kanaya, Tadashi Kaneko, Keita Kanehata, Ken-ichi Kano, Hiroyuki Kawano, Kazuya Kikutani, Hitoshi Kikuchi, Takahiro Kido, Sho Kimura, Hiroyuki Koami, Daisuke Kobashi, Iwao Saiki, Masahito Sakai, Ayaka Sakamoto, Tetsuya Sato, Yasuhiro Shiga, Manabu Shimoto, Shinya Shimoyama, Tomohisa Shoko, Yoh Sugawara, Atsunori Sugita, Satoshi Suzuki, Yuji Suzuki, Tomohiro Suhara, Kenji Sonota, Shuhei Takauji, Kohei Takashima, Sho Takahashi, Yoko Takahashi, Jun Takeshita, Yuuki Tanaka, Akihito Tampo, Taichiro Tsunoyama, Kenichi Tetsuhara, Kentaro Tokunaga, Yoshihiro Tomioka, Kentaro Tomita, Naoki Tominaga, Mitsunobu Toyosaki, Yukitoshi Toyoda, Hiromichi Naito, Isao Nagata, Tadashi Nagato, Yoshimi Nakamura, Yuki Nakamori, Isao Nahara, Hiromu Naraba, Chihiro Narita, Norihiro Nishioka, Tomoya Nishimura, Kei Nishiyama, Tomohisa Nomura, Taiki Haga, Yoshihiro Hagiwara, Katsuhiko Hashimoto, Takeshi Hatachi, Toshiaki Hamasaki, Takuya Hayashi, Minoru Hayashi, Atsuki Hayamizu, Go Haraguchi, Yohei Hirano, Ryo Fujii, Motoki Fujita, Naoyuki Fujimura, Hiraku Funakoshi, Masahito Horiguchi, Jun Maki, Naohisa Masunaga, Yosuke Matsumura, Takuya Mayumi, Keisuke Minami, Yuya Miyazaki, Kazuyuki Miyamoto, Teppei Murata, Machi Yanai, Takao Yano, Kohei Yamada, Naoki Yamada, Tomonori Yamamoto, Shodai Yoshihiro, Hiroshi Tanaka & Osamu Nishid